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WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions
Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not c...
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| Published in: | IEEE sensors journal 2022-03, Vol.22 (6), p.5122-5132 |
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| creator | Kachurka, Viachaslau Rault, Bastien Ireta Munoz, Fernando I. Roussel, David Bonardi, Fabien Didier, Jean-Yves Hadj-Abdelkader, Hicham Bouchafa, Samia Alliez, Pierre Robin, Maxime |
| description | Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details. |
| doi_str_mv | 10.1109/JSEN.2021.3101121 |
| format | article |
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However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2021.3101121</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Automatic ; Autonomous navigation ; Communication ; Computer Science ; Computer vision ; Computer Vision and Pattern Recognition ; embedded software ; Emergency response ; Engineering Sciences ; Global positioning systems ; GPS ; Indoor environments ; indoor navigation ; Inertial sensing devices ; Laser radar ; Lidar ; Localization ; Localization method ; Location awareness ; Multiple robots ; Optimization ; Real time ; Real-time systems ; Sensor fusion ; Sensors ; Signal and Image processing ; Simultaneous localization and mapping ; terrain mapping ; Three-dimensional displays ; Tracking systems</subject><ispartof>IEEE sensors journal, 2022-03, Vol.22 (6), p.5122-5132</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c370t-81605cf858ec98b6003aaa5f157ca75700f6661464dd4f89327f80061ea4d3803</citedby><cites>FETCH-LOGICAL-c370t-81605cf858ec98b6003aaa5f157ca75700f6661464dd4f89327f80061ea4d3803</cites><orcidid>0000-0002-2860-8128 ; 0000-0003-2088-6067 ; 0000-0002-1839-0831 ; 0000-0002-6214-4005 ; 0000-0002-3555-7306 ; 0000-0002-9863-5471 ; 0000-0001-9944-4602</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9500208$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,315,786,790,891,27957,27958,55147</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03609471$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Kachurka, Viachaslau</creatorcontrib><creatorcontrib>Rault, Bastien</creatorcontrib><creatorcontrib>Ireta Munoz, Fernando I.</creatorcontrib><creatorcontrib>Roussel, David</creatorcontrib><creatorcontrib>Bonardi, Fabien</creatorcontrib><creatorcontrib>Didier, Jean-Yves</creatorcontrib><creatorcontrib>Hadj-Abdelkader, Hicham</creatorcontrib><creatorcontrib>Bouchafa, Samia</creatorcontrib><creatorcontrib>Alliez, Pierre</creatorcontrib><creatorcontrib>Robin, Maxime</creatorcontrib><title>WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.</description><subject>Automatic</subject><subject>Autonomous navigation</subject><subject>Communication</subject><subject>Computer Science</subject><subject>Computer vision</subject><subject>Computer Vision and Pattern Recognition</subject><subject>embedded software</subject><subject>Emergency response</subject><subject>Engineering Sciences</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Indoor environments</subject><subject>indoor navigation</subject><subject>Inertial sensing devices</subject><subject>Laser radar</subject><subject>Lidar</subject><subject>Localization</subject><subject>Localization method</subject><subject>Location awareness</subject><subject>Multiple robots</subject><subject>Optimization</subject><subject>Real time</subject><subject>Real-time systems</subject><subject>Sensor fusion</subject><subject>Sensors</subject><subject>Signal and Image processing</subject><subject>Simultaneous localization and mapping</subject><subject>terrain mapping</subject><subject>Three-dimensional displays</subject><subject>Tracking systems</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9kEFLw0AQhYMoWKs_QLwsePKQOpNNNrveSqhWiQq2pd6WbTKpKWm2blJBf70JFU8zzPvm8Xied4kwQgR1-zSbvIwCCHDEERADPPIGGEXSxziUx_3OwQ95_H7qnTXNBgBVHMUDb7OkxPqzdPx8x5ZknFlVxBJrd-RMW34R6yU2-25a2rLCOvZGpvLn5ZbYY53b7pDazFTlT0fbmi3qnBxLPkxVUb0u63XnVedlrzXn3klhqoYu_ubQW9xP5snUT18fHpNx6mc8htaXKCDKChlJypRcCQBujIkKjOLMdJkBCiEEhiLM87CQigdxIQEEkglzLoEPvZuDb5dC71y5Ne5bW1Pq6TjV_Q24ABXG-IUde31gd85-7qlp9cbuXd3F04HgSnGhRE_hgcqcbRpHxb8tgu7r1339uq9f_9Xf_Vwdfkoi-udVBBCA5L8NmH5h</recordid><startdate>20220315</startdate><enddate>20220315</enddate><creator>Kachurka, Viachaslau</creator><creator>Rault, Bastien</creator><creator>Ireta Munoz, Fernando I.</creator><creator>Roussel, David</creator><creator>Bonardi, Fabien</creator><creator>Didier, Jean-Yves</creator><creator>Hadj-Abdelkader, Hicham</creator><creator>Bouchafa, Samia</creator><creator>Alliez, Pierre</creator><creator>Robin, Maxime</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-2860-8128</orcidid><orcidid>https://orcid.org/0000-0003-2088-6067</orcidid><orcidid>https://orcid.org/0000-0002-1839-0831</orcidid><orcidid>https://orcid.org/0000-0002-6214-4005</orcidid><orcidid>https://orcid.org/0000-0002-3555-7306</orcidid><orcidid>https://orcid.org/0000-0002-9863-5471</orcidid><orcidid>https://orcid.org/0000-0001-9944-4602</orcidid></search><sort><creationdate>20220315</creationdate><title>WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions</title><author>Kachurka, Viachaslau ; Rault, Bastien ; Ireta Munoz, Fernando I. ; Roussel, David ; Bonardi, Fabien ; Didier, Jean-Yves ; Hadj-Abdelkader, Hicham ; Bouchafa, Samia ; Alliez, Pierre ; Robin, Maxime</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c370t-81605cf858ec98b6003aaa5f157ca75700f6661464dd4f89327f80061ea4d3803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Automatic</topic><topic>Autonomous navigation</topic><topic>Communication</topic><topic>Computer Science</topic><topic>Computer vision</topic><topic>Computer Vision and Pattern Recognition</topic><topic>embedded software</topic><topic>Emergency response</topic><topic>Engineering Sciences</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Indoor environments</topic><topic>indoor navigation</topic><topic>Inertial sensing devices</topic><topic>Laser radar</topic><topic>Lidar</topic><topic>Localization</topic><topic>Localization method</topic><topic>Location awareness</topic><topic>Multiple robots</topic><topic>Optimization</topic><topic>Real time</topic><topic>Real-time systems</topic><topic>Sensor fusion</topic><topic>Sensors</topic><topic>Signal and Image processing</topic><topic>Simultaneous localization and mapping</topic><topic>terrain mapping</topic><topic>Three-dimensional displays</topic><topic>Tracking systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kachurka, Viachaslau</creatorcontrib><creatorcontrib>Rault, Bastien</creatorcontrib><creatorcontrib>Ireta Munoz, Fernando I.</creatorcontrib><creatorcontrib>Roussel, David</creatorcontrib><creatorcontrib>Bonardi, Fabien</creatorcontrib><creatorcontrib>Didier, Jean-Yves</creatorcontrib><creatorcontrib>Hadj-Abdelkader, Hicham</creatorcontrib><creatorcontrib>Bouchafa, Samia</creatorcontrib><creatorcontrib>Alliez, Pierre</creatorcontrib><creatorcontrib>Robin, Maxime</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kachurka, Viachaslau</au><au>Rault, Bastien</au><au>Ireta Munoz, Fernando I.</au><au>Roussel, David</au><au>Bonardi, Fabien</au><au>Didier, Jean-Yves</au><au>Hadj-Abdelkader, Hicham</au><au>Bouchafa, Samia</au><au>Alliez, Pierre</au><au>Robin, Maxime</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2022-03-15</date><risdate>2022</risdate><volume>22</volume><issue>6</issue><spage>5122</spage><epage>5132</epage><pages>5122-5132</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Real-time globally consistent GPS tracking is critical for an accurate localization and is crucial for applications such as autonomous navigation or multi-robot mapping. However, under challenging environment conditions such as indoor/outdoor transitions, GPS signals are partially available or not consistent over time. In this paper, a real-time tracking system for continuously locating emergency response agents in challenging conditions is presented. A cooperative localization method based on Laser-Visual-Inertial (LVI) and GPS sensors is achieved by communicating optimization events between a LiDAR-Inertial-SLAM (LI-SLAM) and Visual-Inertial-SLAM (VI-SLAM) that operate simultaneously. The estimation of the pose assisted by multiple SLAM approaches provides the GPS localization of the agent when a stand-alone GPS fails. The system has been tested under the terms of the MALIN Challenge, which aims to globally localize agents across outdoor and indoor environments under challenging conditions (such as smoked rooms, stairs, indoor/outdoor transitions, repetitive patterns, extreme lighting changes) where it is well known that a stand-alone SLAM will not be enough to maintaining the localization. The system achieved Absolute Trajectory Error of 0.48%, with a pose update rate between 15 and 20 Hz. Furthermore, the system is able to build a global consistent 3D LiDAR Map that is post-processed to create a 3D reconstruction at different level of details.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2021.3101121</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-2860-8128</orcidid><orcidid>https://orcid.org/0000-0003-2088-6067</orcidid><orcidid>https://orcid.org/0000-0002-1839-0831</orcidid><orcidid>https://orcid.org/0000-0002-6214-4005</orcidid><orcidid>https://orcid.org/0000-0002-3555-7306</orcidid><orcidid>https://orcid.org/0000-0002-9863-5471</orcidid><orcidid>https://orcid.org/0000-0001-9944-4602</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Automatic Autonomous navigation Communication Computer Science Computer vision Computer Vision and Pattern Recognition embedded software Emergency response Engineering Sciences Global positioning systems GPS Indoor environments indoor navigation Inertial sensing devices Laser radar Lidar Localization Localization method Location awareness Multiple robots Optimization Real time Real-time systems Sensor fusion Sensors Signal and Image processing Simultaneous localization and mapping terrain mapping Three-dimensional displays Tracking systems |
| title | WeCo-SLAM: Wearable Cooperative SLAM System for Real-Time Indoor Localization Under Challenging Conditions |
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