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The Semantic Point & Line SLAM for Indoor Dynamic Environment
Simultaneous Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. In the past few years, many excellent visual SLAM systems were born, and most of them can do a good job in a static environment. However, in dynamic scenes,...
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creator | Zhenghang, Guo Xinchun, Ji Dongyan, Wei Chao, Xie Jingyu, Zhang |
description | Simultaneous Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. In the past few years, many excellent visual SLAM systems were born, and most of them can do a good job in a static environment. However, in dynamic scenes, unreliable feature points in the scene will lead to the decline of system positioning accuracy and even cause system failure. Traditional methods often use the removal of dynamic points to solve dynamic scene problems, but in some environments, the reduction of feature points will also affect the positioning accuracy. Therefore, based on the ORB-SLAM2 visual SLAM system, this paper proposes a semantic point and line SLAM system for an indoor dynamic environment. The improved SLAM system has good performance in an indoor dynamic environment. Finally, we evaluate our algorithm on the TUM RGB-D dynamic dataset. The results show that the absolute Trajectory Accuracy of SPL-SLAM is significantly improved compared with the original ORB-SLAM2. |
doi_str_mv | 10.1109/IPIN54987.2022.9918122 |
format | conference_proceeding |
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The results show that the absolute Trajectory Accuracy of SPL-SLAM is significantly improved compared with the original ORB-SLAM2.</description><identifier>EISSN: 2471-917X</identifier><identifier>EISBN: 9781728162188</identifier><identifier>EISBN: 1728162181</identifier><identifier>DOI: 10.1109/IPIN54987.2022.9918122</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cameras ; Dynamic ; Heuristic algorithms ; Indoor navigation ; Point-Line Features ; Pose estimation ; Semantic SLAM ; Semantics ; Simultaneous localization and mapping ; Visualization</subject><ispartof>2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2022, p.1-7</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9918122$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,27906,54536,54913</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9918122$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhenghang, Guo</creatorcontrib><creatorcontrib>Xinchun, Ji</creatorcontrib><creatorcontrib>Dongyan, Wei</creatorcontrib><creatorcontrib>Chao, Xie</creatorcontrib><creatorcontrib>Jingyu, Zhang</creatorcontrib><title>The Semantic Point & Line SLAM for Indoor Dynamic Environment</title><title>2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)</title><addtitle>IPIN</addtitle><description>Simultaneous Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. 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The results show that the absolute Trajectory Accuracy of SPL-SLAM is significantly improved compared with the original ORB-SLAM2.</description><subject>Cameras</subject><subject>Dynamic</subject><subject>Heuristic algorithms</subject><subject>Indoor navigation</subject><subject>Point-Line Features</subject><subject>Pose estimation</subject><subject>Semantic SLAM</subject><subject>Semantics</subject><subject>Simultaneous localization and mapping</subject><subject>Visualization</subject><issn>2471-917X</issn><isbn>9781728162188</isbn><isbn>1728162181</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj01Lw0AURUdBsNT8AkGycpf43svHvFm4KLXVQNSCFdyVycwER8xEkiD03xuwcOHA5XDhCnGDkCKCuqt21UuRK5YpAVGqFDISnYlISUZJjCUh87lYUC4xUSg_LkU0jl8AgCViCbwQ9_tPF7-5TofJm3jX-zDFt3Htw9zWq-e47Ye4Craf8XAMupulTfj1Qx86F6YrcdHq79FFJy7F-3azXz8l9etjtV7ViSfIpgQJrcpKxxLJcGZs25SmMXOkYmtJKa0RbGELVkBgIAfQuXQSjCwsN9lSXP_veufc4WfwnR6Oh9Ph7A_DEkjU</recordid><startdate>20220905</startdate><enddate>20220905</enddate><creator>Zhenghang, Guo</creator><creator>Xinchun, Ji</creator><creator>Dongyan, Wei</creator><creator>Chao, Xie</creator><creator>Jingyu, Zhang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20220905</creationdate><title>The Semantic Point & Line SLAM for Indoor Dynamic Environment</title><author>Zhenghang, Guo ; Xinchun, Ji ; Dongyan, Wei ; Chao, Xie ; Jingyu, Zhang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-121d936e8712c83cdfb6cbccbc798dd299aa10d5d589020c0400a47e70c75d8b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cameras</topic><topic>Dynamic</topic><topic>Heuristic algorithms</topic><topic>Indoor navigation</topic><topic>Point-Line Features</topic><topic>Pose estimation</topic><topic>Semantic SLAM</topic><topic>Semantics</topic><topic>Simultaneous localization and mapping</topic><topic>Visualization</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhenghang, Guo</creatorcontrib><creatorcontrib>Xinchun, Ji</creatorcontrib><creatorcontrib>Dongyan, Wei</creatorcontrib><creatorcontrib>Chao, Xie</creatorcontrib><creatorcontrib>Jingyu, Zhang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore (Online service)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhenghang, Guo</au><au>Xinchun, Ji</au><au>Dongyan, Wei</au><au>Chao, Xie</au><au>Jingyu, Zhang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The Semantic Point & Line SLAM for Indoor Dynamic Environment</atitle><btitle>2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN)</btitle><stitle>IPIN</stitle><date>2022-09-05</date><risdate>2022</risdate><spage>1</spage><epage>7</epage><pages>1-7</pages><eissn>2471-917X</eissn><eisbn>9781728162188</eisbn><eisbn>1728162181</eisbn><abstract>Simultaneous Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. In the past few years, many excellent visual SLAM systems were born, and most of them can do a good job in a static environment. However, in dynamic scenes, unreliable feature points in the scene will lead to the decline of system positioning accuracy and even cause system failure. Traditional methods often use the removal of dynamic points to solve dynamic scene problems, but in some environments, the reduction of feature points will also affect the positioning accuracy. Therefore, based on the ORB-SLAM2 visual SLAM system, this paper proposes a semantic point and line SLAM system for an indoor dynamic environment. The improved SLAM system has good performance in an indoor dynamic environment. Finally, we evaluate our algorithm on the TUM RGB-D dynamic dataset. The results show that the absolute Trajectory Accuracy of SPL-SLAM is significantly improved compared with the original ORB-SLAM2.</abstract><pub>IEEE</pub><doi>10.1109/IPIN54987.2022.9918122</doi><tpages>7</tpages></addata></record> |
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subjects | Cameras Dynamic Heuristic algorithms Indoor navigation Point-Line Features Pose estimation Semantic SLAM Semantics Simultaneous localization and mapping Visualization |
title | The Semantic Point & Line SLAM for Indoor Dynamic Environment |
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