{"id":6200,"date":"2024-01-12T19:07:26","date_gmt":"2024-01-12T11:07:26","guid":{"rendered":"https:\/\/www.nooploop.com\/?page_id=6200"},"modified":"2024-07-02T20:06:40","modified_gmt":"2024-07-02T12:06:40","slug":"paper","status":"publish","type":"page","link":"https:\/\/www.nooploop.com\/cn\/paper\/","title":{"rendered":"\u57fa\u4e8eUWB\u9ad8\u7cbe\u5ea6\u5b9a\u4f4d\u7cfb\u7edf\u7684\u8bba\u6587\u6210\u529f\u96c6\u9526"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container hundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"background-color: #282828;background-position: center center;background-repeat: no-repeat;border-width: 0px 0px 0px 0px;border-color:#3e3e3e;border-style:solid;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start\" style=\"width:104% !important;max-width:104% !important;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-1\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.abm5954\"><img class=\"size-full wp-image-6206 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Swarm-of-micro-flying-robots-in-the-wild.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Swarm-of-micro-flying-robots-in-the-wild-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Swarm-of-micro-flying-robots-in-the-wild-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Swarm-of-micro-flying-robots-in-the-wild.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/www.science.org\/doi\/10.1126\/scirobotics.abm5954\"><b style=\"font-size: 16px;\" data-fusion-font=\"true\">Swarm of Micro Flying Robots in The Wild<\/b><\/a><\/h4>\n<p style=\"font-size: 12px; text-align: center; line-height: 24px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">May 2022, SCIENCE ROBOTICS, XIN ZHOU, Xiangyong Wen&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges. To enable swarm navigation in the wild, we develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-0{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-0 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-0{width:100% !important;order : 0;}.fusion-builder-column-0 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-0{width:100% !important;order : 0;}.fusion-builder-column-0 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-1 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-2\"><h4 class=\"\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2103.04131.pdf\"><img class=\"size-full wp-image-6207 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Omni-swarm\uff1aA-Decentralized-Omnidirectional-Visual-Inertial-UWB-State-Estimation-System-for-Aerial-Swarms.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Omni-swarm\uff1aA-Decentralized-Omnidirectional-Visual-Inertial-UWB-State-Estimation-System-for-Aerial-Swarms-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Omni-swarm\uff1aA-Decentralized-Omnidirectional-Visual-Inertial-UWB-State-Estimation-System-for-Aerial-Swarms-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Omni-swarm\uff1aA-Decentralized-Omnidirectional-Visual-Inertial-UWB-State-Estimation-System-for-Aerial-Swarms.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2103.04131.pdf\" data-fusion-font=\"true\">Omni-Swarm: A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System<\/a><span style=\"color: #929aa3;\"><span style=\"color: #1a80b6;\">&#8230;<\/span><\/span><\/h4>\n<p style=\"font-size: 12px; text-align: center; line-height: 24px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Mar 2021, IEEE T ROBOT, Hao Xu, Yichen Zhang, Boyu Zhou&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Decentralized state estimation is one of the most fundamental components of autonomous aerial swarm systems in GPS-denied areas yet it still remains a highly challenging research topic. Omni-swarm, a decentralized omnidirectional visual-inertial-UWB state estimation system for aerial swarms, is proposed in this paper to address this research niche. To solve the issues of observability, complicated initialization, insufficient accuracy, and lack of global consistency, we introduce an omnidirectional perception front-end in Omni-swarm&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-1{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-1 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-1{width:100% !important;order : 0;}.fusion-builder-column-1 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-1{width:100% !important;order : 0;}.fusion-builder-column-1 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-2 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-3\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2003.05138.pdf\"><img class=\"alignnone size-full wp-image-6208 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Decentralized-Visual-Inertial-UWB-Fusion-for-Relative-State-Estimation-of-Aerial-Swarm.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Decentralized-Visual-Inertial-UWB-Fusion-for-Relative-State-Estimation-of-Aerial-Swarm-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Decentralized-Visual-Inertial-UWB-Fusion-for-Relative-State-Estimation-of-Aerial-Swarm-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Decentralized-Visual-Inertial-UWB-Fusion-for-Relative-State-Estimation-of-Aerial-Swarm.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2003.05138.pdf\"><strong style=\"font-size: 16px;\" data-fusion-font=\"true\">Decentralized Visual-Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm<\/strong><\/a><\/h4>\n<p style=\"text-align: center; font-size: 14px;\" data-fusion-font=\"true\"><span style=\"font-size: 12px; font-weight: normal;\" data-fusion-font=\"true\">Mar 2020, ICRA 2020, Hao Xu, Luqi Wang, Yichen Zhang&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">The collaboration of unmanned aerial vehicles (UAVs) has become a popular research topic for its practicability in multiple scenarios. The collaboration of multiple UAVs, which is also known as aerial swarm is a highly complex system, which still lacks a state-of-art decentralized relative state estimation method. In this paper, we present a novel fully decentralized visual-inertial-UWB fusion framework for relative state estimation and demonstrate the practicability by performing extensive aerial swarm flight experiments&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-2{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-2 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-2{width:100% !important;order : 0;}.fusion-builder-column-2 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-2{width:100% !important;order : 0;}.fusion-builder-column-2 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-3 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-4\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/1642\/1\/012028\/pdf\"><img class=\"alignnone size-full wp-image-6209 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Cooperative-3-D-relative-localization-for-UAV-swarm-by-fusing-UWB-with-IMU-and-GPS.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Cooperative-3-D-relative-localization-for-UAV-swarm-by-fusing-UWB-with-IMU-and-GPS-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Cooperative-3-D-relative-localization-for-UAV-swarm-by-fusing-UWB-with-IMU-and-GPS-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Cooperative-3-D-relative-localization-for-UAV-swarm-by-fusing-UWB-with-IMU-and-GPS.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/1642\/1\/012028\/pdf\" data-fusion-font=\"true\">Cooperative 3-D Relative Localization for UAV Swarm by Fusing UWB with IMU and GPS<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\">Sep 2020, Yang Qi, Yisheng Zhong, Zongying Shi<\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">A real-time 3-D relative position estimator is presented for fixed-wing UAV swarm system in formation flight, using measurements from IMU, compass, GPS, and a set of UWB ranging radios. Instead of using stationary UWB anchors to provide 3-D coordinate and fusing it with IMU, the estimator uses UWB measurements for the construction of a non-convex function, and calculates the global optimum of the non-convex function to get position estimation, while the GPS positions are used to provide bearing information&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-3{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-3 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-3{width:100% !important;order : 0;}.fusion-builder-column-3 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-3{width:100% !important;order : 0;}.fusion-builder-column-3 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-4 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-5\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/ieeexplore.ieee.org\/document\/10161260\"><img class=\"alignnone size-full wp-image-6210 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Model-Predictive-Formation-Control-with-Gait-Synchronization-for-Multiple-Quadruped-Robots.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Model-Predictive-Formation-Control-with-Gait-Synchronization-for-Multiple-Quadruped-Robots-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Model-Predictive-Formation-Control-with-Gait-Synchronization-for-Multiple-Quadruped-Robots-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Model-Predictive-Formation-Control-with-Gait-Synchronization-for-Multiple-Quadruped-Robots.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10161260\" data-fusion-font=\"true\">Distributed Model Predictive Formation Control with Gait<\/a><\/h4>\n<p style=\"text-align: center; font-size: 14px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\"><span style=\"font-size: 12px;\" data-fusion-font=\"true\">May 2023, ICRA 2023, <\/span><span style=\"color: var(--body_typography-color); font-family: var(--body_typography-font-family); font-size: 12px; font-style: var(--body_typography-font-style,normal); letter-spacing: var(--body_typography-letter-spacing);\" data-fusion-font=\"true\">Shaohang Xu, Wentao Zhang&#8230;<\/span><\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">In this paper, we present a fully distributed framework for multiple quadruped robots in environments with obstacles. Our approach utilizes Model Predictive Control (MPC) and multi-robot consensus protocol to obtain the distributed control law. It ensures that all the robots are able to avoid obstacles, navigate to the desired positions, and meanwhile synchronize the gaits. In particular, via MPC and consensus, the robots compute the optimal trajectory and the contact profile of the legs&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-4{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-4 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-4{width:100% !important;order : 0;}.fusion-builder-column-4 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-4{width:100% !important;order : 0;}.fusion-builder-column-4 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-5 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-6\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2209.06779.pdf\"><img class=\"alignnone size-full wp-image-6211 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Ef\ufb01cient-Planar-Pose-Estimation-via-UWB-Measurements.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Ef\ufb01cient-Planar-Pose-Estimation-via-UWB-Measurements-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Ef\ufb01cient-Planar-Pose-Estimation-via-UWB-Measurements-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Ef\ufb01cient-Planar-Pose-Estimation-via-UWB-Measurements.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2209.06779.pdf\" data-fusion-font=\"true\">Efficient Planar Pose Estimation via UWB Measurements<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Feb 2023, ICRA 2023, Haodong Jiang, Wentao Wang&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">State estimation is an essential part of autonomous systems. Integrating the Ultra-Wideband (UWB) technique has been shown to correct the long-term estimation drift and bypass the complexity of loop closure detection. However, few works on robotics treat UWB as a stand-alone state estimation solution. The primary purpose of this work is to investigate planar pose estimation using only UWB range measurements&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-5{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-5 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-5{width:100% !important;order : 0;}.fusion-builder-column-5 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-5{width:100% !important;order : 0;}.fusion-builder-column-5 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-6 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-7\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2203.11004.pdf\"><img class=\"alignnone size-full wp-image-6212 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Multi-Agent-Relative-Pose-Estimation-with-UWB-and-Constrained-Communications.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Multi-Agent-Relative-Pose-Estimation-with-UWB-and-Constrained-Communications-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Multi-Agent-Relative-Pose-Estimation-with-UWB-and-Constrained-Communications-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Multi-Agent-Relative-Pose-Estimation-with-UWB-and-Constrained-Communications.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2203.11004.pdf\" data-fusion-font=\"true\">Multi-Agent Relative Pose Estimation with UWB and Constrained Communications<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Mar 2022, IROS 2022, Andrew Fishberg, Jonathan P. How<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">We propose a novel inter-agent relative 2D pose estimation system where each participating agent is equipped with several ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB range measurements with additional continuously transmitted data, such as odometry, making these approaches scale poorly with increased swarm size or decreased communication throughput. This approach addresses these concerns by using only locally collected UWB measurements with no additionally transmitted data&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-6{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-6 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-6{width:100% !important;order : 0;}.fusion-builder-column-6 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-6{width:100% !important;order : 0;}.fusion-builder-column-6 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-7 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-8\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2303.10903.pdf\"><img class=\"alignnone size-full wp-image-6213 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/VR-SLAM\uff1aA-Visual-Range-Simultaneous-Localization-and-Mapping-System-using-Monocular-Camera-and-Ultra-wideband-Sensors.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/VR-SLAM\uff1aA-Visual-Range-Simultaneous-Localization-and-Mapping-System-using-Monocular-Camera-and-Ultra-wideband-Sensors-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/VR-SLAM\uff1aA-Visual-Range-Simultaneous-Localization-and-Mapping-System-using-Monocular-Camera-and-Ultra-wideband-Sensors-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/VR-SLAM\uff1aA-Visual-Range-Simultaneous-Localization-and-Mapping-System-using-Monocular-Camera-and-Ultra-wideband-Sensors.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2303.10903.pdf\" data-fusion-font=\"true\">VR-SLAM: A Visual-Range Simultaneous Localization and Mapping System using<\/a><span style=\"color: #1a80b6;\">&#8230;<\/span><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Mar 2023, Thien Hoang Nguyen, Shenghai Yuan, Lihua Xie<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">In this work, we propose a simultaneous localization and mapping (SLAM) system using a monocular camera and Ultra-wideband (UWB) sensors. Our system, referred to as VR-SLAM, is a multi-stage framework that leverages the strengths and compensates for the weaknesses of each sensor. Firstly, we introduce a UWB-aided 7 degree-of-freedom (scale factor, 3D position, and 3D orientation) global alignment module to initialize the visual odometry (VO) system in the world frame defined by the UWB anchors&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-7{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-7 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-7{width:100% !important;order : 0;}.fusion-builder-column-7 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-7{width:100% !important;order : 0;}.fusion-builder-column-7 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-8 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-9\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2301.12344.pdf\"><img class=\"alignnone size-full wp-image-6214 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/TJ-FlyingFish\uff1aDesign-and-Implementation-of-an-Aerial-Aquatic-Quadrotor-with-Tiltable-Propulsion-Units.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/TJ-FlyingFish\uff1aDesign-and-Implementation-of-an-Aerial-Aquatic-Quadrotor-with-Tiltable-Propulsion-Units-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/TJ-FlyingFish\uff1aDesign-and-Implementation-of-an-Aerial-Aquatic-Quadrotor-with-Tiltable-Propulsion-Units-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/TJ-FlyingFish\uff1aDesign-and-Implementation-of-an-Aerial-Aquatic-Quadrotor-with-Tiltable-Propulsion-Units.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2301.12344.pdf\"><span style=\"font-size: 16px;\" data-fusion-font=\"true\">TJ-FlyingFish: Design and Implementation of an Aerial-Aquatic Quadrotor with Tiltable<\/span><\/a><span style=\"color: #1a80b6;\">&#8230;<\/span><\/h4>\n<p style=\"text-align: center; font-size: 14px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\"><span style=\"font-size: 12px;\" data-fusion-font=\"true\">Feb 2023, ICRA 2023, <\/span><span style=\"color: var(--body_typography-color); font-family: var(--body_typography-font-family); font-size: 12px; font-style: var(--body_typography-font-style,normal); letter-spacing: var(--body_typography-letter-spacing);\" data-fusion-font=\"true\">Xuchen Liu, Minghao Dou&#8230;<\/span><\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Aerial-aquatic vehicles are capable to move in the two most dominant fluids, making them more promising for a wide range of applications. We propose a prototype with special designs for propulsion and thruster configuration to cope with the vast differences in the fluid properties of water and air. For propulsion, the operating range is switched for the different mediums by the dual-speed propulsion unit, providing sufficient thrust and also ensuring output efficiency&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-8{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-8 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-8{width:100% !important;order : 0;}.fusion-builder-column-8 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-8{width:100% !important;order : 0;}.fusion-builder-column-8 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-9 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-10\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2303.08454.pdf\"><img class=\"alignnone size-full wp-image-6215 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Range-Aided-LiDAR-Inertial-Multi-Vehicle-Mapping-in-Degenerate-Environment.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Range-Aided-LiDAR-Inertial-Multi-Vehicle-Mapping-in-Degenerate-Environment-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Range-Aided-LiDAR-Inertial-Multi-Vehicle-Mapping-in-Degenerate-Environment-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Range-Aided-LiDAR-Inertial-Multi-Vehicle-Mapping-in-Degenerate-Environment.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2303.08454.pdf\" data-fusion-font=\"true\">Range-Aided LiDAR-Inertial Multi-Vehicle Mapping in Degenerate Environment<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Mar 2023, Zhe Jin, Chaoyang Jiang<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">This paper presents a range-aided LiDAR-inertial multi-vehicle mapping system (RaLI-Multi). Firstly, we design a multi-metric weights LiDAR-inertial odometry by fusing observations from an inertial measurement unit (IMU) and a light detection and ranging sensor (LiDAR). The degenerate level and direction are evaluated by analyzing the distribution of normal vectors of feature point clouds and are used to activate the degeneration correction module in which range measurements correct the pose estimation from the degeneration direction&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-9{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-9 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-9{width:100% !important;order : 0;}.fusion-builder-column-9 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-9{width:100% !important;order : 0;}.fusion-builder-column-9 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-10 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-11\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2303.01242.pdf\"><img class=\"alignnone size-full wp-image-6216 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Optimization-in-Sensor-Network-for-Scalable-Multi-Robot-Relative-State-Estimation.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Optimization-in-Sensor-Network-for-Scalable-Multi-Robot-Relative-State-Estimation-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Optimization-in-Sensor-Network-for-Scalable-Multi-Robot-Relative-State-Estimation-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Optimization-in-Sensor-Network-for-Scalable-Multi-Robot-Relative-State-Estimation.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2303.01242.pdf\" data-fusion-font=\"true\">Distributed Optimization in Sensor Network for Scalable Multi-Robot Relative State Estimation<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Sep 2023, Tianyue Wu, Fei Gao<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Distance measurements demonstrate distinctive scalability when used for relative state estimation in large-scale multirobot systems. Despite the attractiveness of distance measurements, multi-robot relative state estimation based on distance measurements raises a tricky optimization problem, especially in the context of large-scale systems. Motivated by this, we aim to develop specialized computational techniques that enable robust and efficient estimation when deploying distance measurements at scale&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-10{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-10 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-10{width:100% !important;order : 0;}.fusion-builder-column-10 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-10{width:100% !important;order : 0;}.fusion-builder-column-10 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-11 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-12\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2211.03093.pdf\"><img class=\"alignnone size-full wp-image-6217 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/SRIBO\uff1aAn-Efficient-and-Resilient-Single-Range-and-Inertia-Based-Odometry-for-Flying-Robots.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/SRIBO\uff1aAn-Efficient-and-Resilient-Single-Range-and-Inertia-Based-Odometry-for-Flying-Robots-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/SRIBO\uff1aAn-Efficient-and-Resilient-Single-Range-and-Inertia-Based-Odometry-for-Flying-Robots-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/SRIBO\uff1aAn-Efficient-and-Resilient-Single-Range-and-Inertia-Based-Odometry-for-Flying-Robots.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2211.03093.pdf\" data-fusion-font=\"true\">SRIBO: An Efficient and Resilient Single-Range and Inertia Based Odometry for Flying Robots<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Nov 2022, Wei Dong, Zheyuan Mei, Yuanjiong Ying&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Positioning with one inertial measurement unit and one ranging sensor is commonly thought to be feasible only when trajectories are in certain patterns ensuring observability. For this reason, to pursue observable patterns, it is required either exciting the trajectory or searching key nodes in a long interval, which is commonly highly nonlinear and may also lack resilience. Therefore, such a positioning approach is still not widely accepted in real-world applications&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-11{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-11 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-11{width:100% !important;order : 0;}.fusion-builder-column-11 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-11{width:100% !important;order : 0;}.fusion-builder-column-11 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-12 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-13\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2210.16086.pdf\"><img class=\"alignnone size-full wp-image-6218 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/KD-EKF\uff1aA-Kalman-Decomposition-Based-Extended-Kalman-Filter-for-Multi-Robot-Cooperative-Localization.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/KD-EKF\uff1aA-Kalman-Decomposition-Based-Extended-Kalman-Filter-for-Multi-Robot-Cooperative-Localization-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/KD-EKF\uff1aA-Kalman-Decomposition-Based-Extended-Kalman-Filter-for-Multi-Robot-Cooperative-Localization-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/KD-EKF\uff1aA-Kalman-Decomposition-Based-Extended-Kalman-Filter-for-Multi-Robot-Cooperative-Localization.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2210.16086.pdf\" data-fusion-font=\"true\">KD-EKF: A Kalman Decomposition Based Extended Kalman Filter for Multi-Robot Cooperative<\/a><span style=\"color: #1a80b6;\">&#8230;<\/span><\/h4>\n<p style=\"text-align: center;\"><span style=\"font-size: 12px; font-weight: normal;\" data-fusion-font=\"true\">Oct 2022, Ning Hao, Fenghua He, Chungeng Tian&#8230;<\/span><\/p>\n<p style=\"text-align: left;\"><span style=\"font-size: 14px;\" data-fusion-font=\"true\">This paper investigates the consistency problem of EKF-based cooperative localization (CL) from the perspective of Kalman decomposition, which decomposes the observable and unobservable states and allows treating them individually. The factors causing the dimension reduction of the unobservable subspace, termed <\/span><i><span style=\"font-size: 14px;\" data-fusion-font=\"true\">error discrepancy items<\/span><\/i><span style=\"font-size: 14px;\" data-fusion-font=\"true\">, are explicitly isolated and identified in the state propagation and measurement Jacobians for the first time&#8230;<\/span><\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-12{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-12 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-12{width:100% !important;order : 0;}.fusion-builder-column-12 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-12{width:100% !important;order : 0;}.fusion-builder-column-12 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-13 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-14\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2206.09607.pdf\"><img class=\"alignnone size-full wp-image-6219 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/NLOS-Ranging-Mitigation-with-Neural-Network-Model-for-UWB-Localization.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/NLOS-Ranging-Mitigation-with-Neural-Network-Model-for-UWB-Localization-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/NLOS-Ranging-Mitigation-with-Neural-Network-Model-for-UWB-Localization-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/NLOS-Ranging-Mitigation-with-Neural-Network-Model-for-UWB-Localization.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2206.09607.pdf\" data-fusion-font=\"true\">NLOS Ranging Mitigation with Neural Network Model for UWB Localization<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Jun 2022, IEEE CASE 2022, Muhammad Shalihan, Ran Liu&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Localization of robots is vital for navigation and path planning, such as in cases where a map of the environment is needed. Ultra-Wideband (UWB) for indoor location systems has been gaining popularity over the years with the introduction of low-cost UWB modules providing centimetre-level accuracy. However, in the presence of obstacles in the environment, Non-Line-Of-Sight (NLOS) measurements from the UWB will produce inaccurate results&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-13{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-13 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-13{width:100% !important;order : 0;}.fusion-builder-column-13 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-13{width:100% !important;order : 0;}.fusion-builder-column-13 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-14 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-15\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2112.13369.pdf\"><img class=\"alignnone size-full wp-image-6220 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Stop-Line-Aided-Cooperative-Positioning-of-Connected-Vehicles.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Stop-Line-Aided-Cooperative-Positioning-of-Connected-Vehicles-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Stop-Line-Aided-Cooperative-Positioning-of-Connected-Vehicles-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Stop-Line-Aided-Cooperative-Positioning-of-Connected-Vehicles.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2112.13369.pdf\" data-fusion-font=\"true\">Stop Line Aided Cooperative Positioning of Connected Vehicles<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Dec 2021, <\/span><span style=\"color: var(--body_typography-color); font-family: var(--body_typography-font-family); font-size: 12px; font-style: var(--body_typography-font-style,normal); font-weight: var(--body_typography-font-weight); letter-spacing: var(--body_typography-letter-spacing);\" data-fusion-font=\"true\">IEEE TIV, <\/span><span style=\"font-weight: normal; color: var(--body_typography-color); font-family: var(--body_typography-font-family); font-style: var(--body_typography-font-style,normal); letter-spacing: var(--body_typography-letter-spacing);\">Xingqi Wang, Chaoyang Jiang, Shuxuan Sheng&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">This paper develops a stop line aided cooperative positioning framework for connected vehicles, which creatively utilizes the location of the stop-line to achieve the positioning enhancement for a vehicular ad-hoc network (VANET) in intersection scenarios via Vehicle-to-Vehicle (V2V) communication. Firstly, a self-positioning correction scheme for the first stopped vehicle is presented, which applied the stop line information as benchmarks to correct the GNSS\/INS positioning results&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-14{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-14 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-14{width:100% !important;order : 0;}.fusion-builder-column-14 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-14{width:100% !important;order : 0;}.fusion-builder-column-14 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-15 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-16\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2108.05505.pdf\"><img class=\"alignnone size-full wp-image-6222 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Agile-Formation-Control-of-Drone-Flocking-Enhanced-with-Active-Vision-based-Relative-Localization.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Agile-Formation-Control-of-Drone-Flocking-Enhanced-with-Active-Vision-based-Relative-Localization-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Agile-Formation-Control-of-Drone-Flocking-Enhanced-with-Active-Vision-based-Relative-Localization-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Agile-Formation-Control-of-Drone-Flocking-Enhanced-with-Active-Vision-based-Relative-Localization.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2108.05505.pdf\" data-fusion-font=\"true\">Agile Formation Control of Drone Flocking Enhanced with Active Vision-based<\/a><span style=\"color: #1a80b6;\">&#8230;<\/span><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Nov 2021, <\/span><span style=\"color: var(--body_typography-color); font-family: var(--body_typography-font-family); font-size: 12px; font-style: var(--body_typography-font-style,normal); font-weight: var(--body_typography-font-weight); letter-spacing: var(--body_typography-letter-spacing);\" data-fusion-font=\"true\">IEEE ROBOT AUTOM LET, <\/span><span style=\"font-weight: normal; color: var(--body_typography-color); font-family: var(--body_typography-font-family); font-style: var(--body_typography-font-style,normal); letter-spacing: var(--body_typography-letter-spacing);\">Peihan Zhang, Gang Chen, Yuzhu Li&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">The vision-based relative localization can provide effective feedback for the cooperation of aerial swarm and has been widely investigated in previous works. However, the limited field of view (FOV) inherently restricts its performance. To cope with this issue, this letter proposes a novel distributed active vision-based relative localization framework and apply it to formation control in aerial swarms&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-15{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-15 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-15{width:100% !important;order : 0;}.fusion-builder-column-15 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-15{width:100% !important;order : 0;}.fusion-builder-column-15 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-16 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-17\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2303.06551.pdf\"><img class=\"size-full wp-image-6231 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Systematic-Evaluation-of-Different-Indoor-.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Systematic-Evaluation-of-Different-Indoor--18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Systematic-Evaluation-of-Different-Indoor--200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Systematic-Evaluation-of-Different-Indoor-.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2303.06551.pdf\"><span style=\"font-size: 16px;\" data-fusion-font=\"true\">A Systematic Evaluation of Different Indoor Localization Methods in Robotic<\/span><\/a><span style=\"color: #1a80b6;\">&#8230;<\/span><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Mar 2023, Zhirui Sun, Weinan Chen, Jiankun Wang&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these components, effective localization is essential for the robot to employ subsequent motion planning and end-effector manipulation because it can provide a correct goal position&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-16{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-16 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-16{width:100% !important;order : 0;}.fusion-builder-column-16 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-16{width:100% !important;order : 0;}.fusion-builder-column-16 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-17 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-18\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/ieeexplore.ieee.org\/document\/10197620\"><img class=\"alignnone size-full wp-image-6223 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Benchmark-of-Absolute-and-Relative-Positioning-Solutions-in-GNSS-Denied-Environments.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Benchmark-of-Absolute-and-Relative-Positioning-Solutions-in-GNSS-Denied-Environments-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Benchmark-of-Absolute-and-Relative-Positioning-Solutions-in-GNSS-Denied-Environments-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/A-Benchmark-of-Absolute-and-Relative-Positioning-Solutions-in-GNSS-Denied-Environments.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10197620\" data-fusion-font=\"true\">A Benchmark of Absolute and Relative Positioning Solutions in GNSS Denied Environments<\/a><\/h4>\n<p style=\"text-align: center; font-size: 14px;\" data-fusion-font=\"true\"><span style=\"font-size: 12px;\" data-fusion-font=\"true\">Jul 2023, IEEE INTERNET THINGS, <\/span><span style=\"color: var(--body_typography-color); font-family: var(--body_typography-font-family); font-size: 12px; font-style: var(--body_typography-font-style,normal); font-weight: var(--body_typography-font-weight); letter-spacing: var(--body_typography-letter-spacing);\" data-fusion-font=\"true\">Haiyun Yao, Xinlian Liang&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Precise positioning is fundamental to the internet of things that delivers insights into everything from large-scale business to ordinary smart life. Accurate localization and positioning in global navigation satellite system (GNSS) denied environments, such as indoor-, underground- spaces, and forests, is one of the most prosperous research fields because of the great complexity prompted by various challenging application scenarios&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-17{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-17 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-17{width:100% !important;order : 0;}.fusion-builder-column-17 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-17{width:100% !important;order : 0;}.fusion-builder-column-17 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-18 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-19\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2203.00356.pdf\"><img class=\"alignnone size-full wp-image-6224 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Indoor-Localization-for-Quadrotors-using-Invisible-Projected-Tags.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Indoor-Localization-for-Quadrotors-using-Invisible-Projected-Tags-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Indoor-Localization-for-Quadrotors-using-Invisible-Projected-Tags-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Indoor-Localization-for-Quadrotors-using-Invisible-Projected-Tags.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2203.00356.pdf\" data-fusion-font=\"true\">Indoor Localization for Quadrotors using Invisible Projected Tags<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Mar 2022, ICRA 2022, Jinjie Li, Liang Han, Zhang Ren<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Augmented reality (AR) technology has been introduced into the robotics field to narrow the visual gap between indoor and outdoor environments. However, without signals from satellite navigation systems, flight experiments in these indoor AR scenarios need other accurate localization approaches. This work proposes a real-time centimeter-level indoor localization method based on psycho-visually invisible projected tags (IPT), requiring a projector as the sender and quadrotors with high-speed cameras as the receiver&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-18{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-18 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-18{width:100% !important;order : 0;}.fusion-builder-column-18 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-18{width:100% !important;order : 0;}.fusion-builder-column-18 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-19 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-20\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2310.10289.pdf\"><img class=\"alignnone size-full wp-image-6225 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Moving-Object-Localization-based-on-the-Fusion-of-Ultra-WideBand-and-LiDAR-with-a-Mobile-Robot.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Moving-Object-Localization-based-on-the-Fusion-of-Ultra-WideBand-and-LiDAR-with-a-Mobile-Robot-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Moving-Object-Localization-based-on-the-Fusion-of-Ultra-WideBand-and-LiDAR-with-a-Mobile-Robot-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Moving-Object-Localization-based-on-the-Fusion-of-Ultra-WideBand-and-LiDAR-with-a-Mobile-Robot.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2310.10289.pdf\" data-fusion-font=\"true\">Moving Object Localization Based on the Fusion of Ultra-WideBand and LiDAR with a Mobile Robot<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Oct 2023, IEEE ROBIO 2023, Muhammad Shalihan, Zhiqiang Cao&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Localization of objects is vital for robot-object interaction. Light Detection and Ranging (LiDAR) application in robotics is an emerging and widely used object localization technique due to its accurate distance measurement, long\u0002range, wide field of view, and robustness in different conditions. However, LiDAR is unable to identify the objects when they are obstructed by obstacles, resulting in inaccuracy and noise in localization&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-19{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-19 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-19{width:100% !important;order : 0;}.fusion-builder-column-19 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-19{width:100% !important;order : 0;}.fusion-builder-column-19 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-20 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-21\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2107.08842.pdf\"><img class=\"alignnone size-full wp-image-6226 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Relative-Localization-of-Mobile-Robots-with-Multiple-Ultra-WideBand-Ranging-Measurements.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Relative-Localization-of-Mobile-Robots-with-Multiple-Ultra-WideBand-Ranging-Measurements-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Relative-Localization-of-Mobile-Robots-with-Multiple-Ultra-WideBand-Ranging-Measurements-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Relative-Localization-of-Mobile-Robots-with-Multiple-Ultra-WideBand-Ranging-Measurements.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2107.08842.pdf\" data-fusion-font=\"true\">Relative Localization of Mobile Robots with Multiple Ultra-WideBand Ranging Measurements<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Jul 2021, IROS 2021, Zhiqiang Cao, Ran Liu, Chau Yuen&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Relative localization between autonomous robots without infrastructure is crucial to achieve their navigation, path planning, and formation in many applications, such as emergency response, where acquiring a prior knowledge of the environment is not possible. The traditional Ultra-WideBand (UWB)-based approach provides a good estimation of the distance between the robots, but obtaining the relative pose (including the displacement and orientation) remains challenging&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-20{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-20 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-20{width:100% !important;order : 0;}.fusion-builder-column-20 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-20{width:100% !important;order : 0;}.fusion-builder-column-20 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-21 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-22\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2302.01036.pdf\"><img class=\"alignnone size-full wp-image-6227 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/CREPES\uff1aCooperative-RElative-Pose-Estimation-System.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/CREPES\uff1aCooperative-RElative-Pose-Estimation-System-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/CREPES\uff1aCooperative-RElative-Pose-Estimation-System-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/CREPES\uff1aCooperative-RElative-Pose-Estimation-System.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2302.01036.pdf\" data-fusion-font=\"true\">CREPES: Cooperative RElative Pose Estimation System<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Mar 2023, IROS 2023, Zhiren Xun, Jian Huang, Zhehan Li&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Mutual localization plays a crucial role in multirobot cooperation. CREPES, a novel system that focuses on six degrees of freedom (DOF) relative pose estimation for multi-robot systems, is proposed in this paper. CREPES has a compact hardware design using active infrared (IR) LEDs, an IR fish-eye camera, an ultra-wideband (UWB) module and an inertial measurement unit (IMU). By leveraging IR light communication, the system solves data association between visual detection and UWB ranging&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-21{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-21 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-21{width:100% !important;order : 0;}.fusion-builder-column-21 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-21{width:100% !important;order : 0;}.fusion-builder-column-21 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-22 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-23\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2311.02937.pdf\"><img class=\"alignnone size-full wp-image-6228 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Marker-Based-Localisation-System-Using-an-Active-PTZ-Camera-and-CNN-Based-Ellipse-Detection.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Marker-Based-Localisation-System-Using-an-Active-PTZ-Camera-and-CNN-Based-Ellipse-Detection-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Marker-Based-Localisation-System-Using-an-Active-PTZ-Camera-and-CNN-Based-Ellipse-Detection-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Marker-Based-Localisation-System-Using-an-Active-PTZ-Camera-and-CNN-Based-Ellipse-Detection.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2311.02937.pdf\" data-fusion-font=\"true\">Marker-Based Localisation System Using an Active PTZ Camera and CNN-Based Ellipse Detection<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Nov 2023, IEEE-ASME T MECH, Xueyan Oh, Ryan Lim&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">Localisation in GPS-denied environments is challenging and many existing solutions have infrastructural and onsite calibration requirements. This paper tackles these challenges by proposing a localisation system that is infrastructure-free and does not require on-site calibration, using a single active PTZ camera to detect, track and localise a circular LED marker&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-22{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-22 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-22{width:100% !important;order : 0;}.fusion-builder-column-22 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-22{width:100% !important;order : 0;}.fusion-builder-column-22 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-23 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-24\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a href=\"https:\/\/arxiv.org\/pdf\/2312.17731.pdf\"><img class=\"alignnone size-full wp-image-6229 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/MURP\uff1aMulti-Agent-Ultra-Wideband-Relative-Pose-Estimation-with-Constrained-Communications-in-3D-Environments.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/MURP\uff1aMulti-Agent-Ultra-Wideband-Relative-Pose-Estimation-with-Constrained-Communications-in-3D-Environments-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/MURP\uff1aMulti-Agent-Ultra-Wideband-Relative-Pose-Estimation-with-Constrained-Communications-in-3D-Environments-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/MURP\uff1aMulti-Agent-Ultra-Wideband-Relative-Pose-Estimation-with-Constrained-Communications-in-3D-Environments.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/a><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2312.17731.pdf\" data-fusion-font=\"true\">MURP: Multi-Agent Ultra-Wideband Relative Pose Estimation with Constrained Communications in<\/a><span style=\"color: #1a80b6;\">&#8230;<\/span><\/h4>\n<p style=\"text-align: center;\"><span style=\"font-size: 12px; font-weight: normal;\" data-fusion-font=\"true\">Dec 2023, Andrew Fishberg, Brian Quiter, Jonathan P. How<\/span><\/p>\n<p style=\"text-align: left;\"><span style=\"font-size: 14px;\" data-fusion-font=\"true\">Inter-agent relative localization is critical for many multi-robot systems operating in the absence of external positioning infrastructure or prior environmental knowledge. We propose a novel inter-agent relative 3D pose estimation system where each participating agent is equipped with several ultra-wideband (UWB) ranging tags. Prior work typically supplements noisy UWB range measurements with additional <\/span><i><span style=\"font-size: 14px;\" data-fusion-font=\"true\">continuously <\/span><\/i><span style=\"font-size: 14px;\" data-fusion-font=\"true\">transmitted data, such as odometry, leading to potential scaling issues with increased team size and\/or decreased communication network capability&#8230;<\/span><\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-23{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-23 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-23{width:100% !important;order : 0;}.fusion-builder-column-23 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-23{width:100% !important;order : 0;}.fusion-builder-column-23 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-24 fusion_builder_column_1_4 1_4 fusion-flex-column\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\" style=\"background-position:left top;background-repeat:no-repeat;-webkit-background-size:cover;-moz-background-size:cover;-o-background-size:cover;background-size:cover;padding: 0px 0px 0px 0px;\"><div class=\"fusion-text fusion-text-25\"><h4 class=\"fusion-responsive-typography-calculated\" style=\"--fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><img class=\"alignnone size-full wp-image-6230 aligncenter\" src=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Ranging-SLAM-for-Multiple-Robots-with-Ultra-WideBand-and-Odometry-Measurements.png\" alt=\"\" width=\"300\" height=\"200\" srcset=\"https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Ranging-SLAM-for-Multiple-Robots-with-Ultra-WideBand-and-Odometry-Measurements-18x12.png 18w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Ranging-SLAM-for-Multiple-Robots-with-Ultra-WideBand-and-Odometry-Measurements-200x133.png 200w, https:\/\/www.nooploop.com\/media\/uploads\/2024\/01\/Distributed-Ranging-SLAM-for-Multiple-Robots-with-Ultra-WideBand-and-Odometry-Measurements.png 300w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/h4>\n<h4 class=\"fusion-responsive-typography-calculated\" style=\"text-align: center; --fontsize: 21; line-height: 1.5; --minfontsize: 21;\" data-fontsize=\"21\" data-lineheight=\"31.5px\"><a style=\"font-size: 16px;\" href=\"https:\/\/arxiv.org\/pdf\/2207.03700.pdf\" data-fusion-font=\"true\">Distributed Ranging SLAM for Multiple Robots with Ultra-WideBand and Odometry Measurements<\/a><\/h4>\n<p style=\"text-align: center; font-size: 12px;\" data-fusion-font=\"true\"><span style=\"font-weight: normal;\">Jul 2022, IROS 2022, Ran Liu, Zhongyuan Deng, Zhiqiang Cao&#8230;<\/span><\/p>\n<p style=\"text-align: left; font-size: 14px;\" data-fusion-font=\"true\">To accomplish task efficiently in a multiple robots system, a problem that has to be addressed is Simultaneous Localization and Mapping (SLAM). LiDAR (Light Detection and Ranging) has been used for many SLAM solutions due to its superb accuracy, but its performance degrades in featureless environments, like tunnels or long corridors. Centralized SLAM solves the problem with a cloud server, which requires a huge amount of computational resources and lacks robustness against central node failure&#8230;<\/p>\n<\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-builder-column-24{width:25% !important;margin-top : 0px;margin-bottom : 0px;}.fusion-builder-column-24 > .fusion-column-wrapper {padding-top : 0px !important;padding-right : 0px !important;margin-right : 7.68%;padding-bottom : 0px !important;padding-left : 0px !important;margin-left : 7.68%;}@media only screen and (max-width:1024px) {.fusion-body .fusion-builder-column-24{width:100% !important;order : 0;}.fusion-builder-column-24 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}@media only screen and (max-width:640px) {.fusion-body .fusion-builder-column-24{width:100% !important;order : 0;}.fusion-builder-column-24 > .fusion-column-wrapper {margin-right : 1.92%;margin-left : 1.92%;}}<\/style><\/div><\/div><style type=\"text\/css\">.fusion-body .fusion-flex-container.fusion-builder-row-1{ padding-top : 30px;margin-top : 0px;padding-right : 2%;padding-bottom : 0px;margin-bottom : 0px;padding-left : 2%;}<\/style><\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"100-width.php","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/pages\/6200"}],"collection":[{"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/comments?post=6200"}],"version-history":[{"count":24,"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/pages\/6200\/revisions"}],"predecessor-version":[{"id":6544,"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/pages\/6200\/revisions\/6544"}],"wp:attachment":[{"href":"https:\/\/www.nooploop.com\/cn\/wp-json\/wp\/v2\/media?parent=6200"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}