A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy
Accurate localization is essential for robot autonomous navigation. The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS w...
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Veröffentlicht in: | IEEE sensors journal 2024-11, Vol.24 (22), p.37846-37858 |
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description | Accurate localization is essential for robot autonomous navigation. The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS with a particle filter, presenting a novel method based on a two-step particle adjustment strategy. Our algorithm first uses GNSS data to evaluate the current particles and adjust their distribution if necessary. Subsequently, we use laser measurements to evaluate old particles and the reliability of the GNSS data, adjusting the particle distribution for correction. In addition, we use statistical features of point clouds for laser measurements, which transform the global map into a series of normal distribution models, and use these models to match with 3-D laser scans for particle state evaluation. Our method improves the processing efficiency of 3-D point cloud data and fully utilizes its 3-D features during localization. Experimental results demonstrate that our algorithm achieves higher localization accuracy on the publicly available KITTI dataset and in real campus environments. In addition, our algorithm consistently delivers precise localization in both open areas and GNSS-unavailable scenarios, showcasing superior reliability. |
doi_str_mv | 10.1109/JSEN.2024.3472470 |
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The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS with a particle filter, presenting a novel method based on a two-step particle adjustment strategy. Our algorithm first uses GNSS data to evaluate the current particles and adjust their distribution if necessary. Subsequently, we use laser measurements to evaluate old particles and the reliability of the GNSS data, adjusting the particle distribution for correction. In addition, we use statistical features of point clouds for laser measurements, which transform the global map into a series of normal distribution models, and use these models to match with 3-D laser scans for particle state evaluation. Our method improves the processing efficiency of 3-D point cloud data and fully utilizes its 3-D features during localization. Experimental results demonstrate that our algorithm achieves higher localization accuracy on the publicly available KITTI dataset and in real campus environments. In addition, our algorithm consistently delivers precise localization in both open areas and GNSS-unavailable scenarios, showcasing superior reliability.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2024.3472470</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Autonomous navigation ; Global navigation satellite system ; Global navigation satellite system (GNSS) ; Laser modes ; Laser radar ; Lasers ; LiDAR ; Localization ; Localization method ; Location awareness ; Measurement by laser beam ; Normal distribution ; Occlusion ; outdoor mobile robot ; Point cloud compression ; Reliability ; robot localization ; Robot sensing systems ; Robots ; sensor fusion ; Sensors ; Statistical analysis ; Three dimensional models ; Three-dimensional displays ; Urban environments</subject><ispartof>IEEE sensors journal, 2024-11, Vol.24 (22), p.37846-37858</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0009-0003-8670-6943 ; 0000-0002-7381-5046 ; 0000-0002-4142-5166 ; 0009-0007-7486-0598</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10709853$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10709853$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tang, Wei</creatorcontrib><creatorcontrib>Huang, Anmin</creatorcontrib><creatorcontrib>Liu, Enbo</creatorcontrib><creatorcontrib>Wu, Jiale</creatorcontrib><creatorcontrib>Zhang, Renyuan</creatorcontrib><title>A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>Accurate localization is essential for robot autonomous navigation. The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS with a particle filter, presenting a novel method based on a two-step particle adjustment strategy. Our algorithm first uses GNSS data to evaluate the current particles and adjust their distribution if necessary. Subsequently, we use laser measurements to evaluate old particles and the reliability of the GNSS data, adjusting the particle distribution for correction. In addition, we use statistical features of point clouds for laser measurements, which transform the global map into a series of normal distribution models, and use these models to match with 3-D laser scans for particle state evaluation. Our method improves the processing efficiency of 3-D point cloud data and fully utilizes its 3-D features during localization. Experimental results demonstrate that our algorithm achieves higher localization accuracy on the publicly available KITTI dataset and in real campus environments. In addition, our algorithm consistently delivers precise localization in both open areas and GNSS-unavailable scenarios, showcasing superior reliability.</description><subject>Algorithms</subject><subject>Autonomous navigation</subject><subject>Global navigation satellite system</subject><subject>Global navigation satellite system (GNSS)</subject><subject>Laser modes</subject><subject>Laser radar</subject><subject>Lasers</subject><subject>LiDAR</subject><subject>Localization</subject><subject>Localization method</subject><subject>Location awareness</subject><subject>Measurement by laser beam</subject><subject>Normal distribution</subject><subject>Occlusion</subject><subject>outdoor mobile robot</subject><subject>Point cloud compression</subject><subject>Reliability</subject><subject>robot localization</subject><subject>Robot sensing systems</subject><subject>Robots</subject><subject>sensor fusion</subject><subject>Sensors</subject><subject>Statistical analysis</subject><subject>Three dimensional models</subject><subject>Three-dimensional displays</subject><subject>Urban environments</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1PwkAQhhujiYj-ABMPm3gu7le77bEqoAbRUEi8NfsxxRLoQneJwV9vGzg4l5nD876TPEFwS_CAEJw-vOXD6YBiygeMC8oFPgt6JIqSkAienHc3wyFn4usyuHJuhTFJRSR6wS5DM1hXUq0BzayyHk2sluvqV_rK1ugd_Lc1aOGqeokm1XM2Q7I2aDzNczTauw55lA4Mag-J5j82zD1s0adsfKXbysys9s5voPYo9430sDxcBxelXDu4Oe1-sBgN508v4eRj_PqUTUJNROxDmqaGkBRIqhRTlCWSa8PB4AiEJlxobbApNVXCJDwplaJCRgmUypRxTA1m_eD-2Ltt7G4Pzhcru2_q9mXBCBXtxIS2FDlSurHONVAW26bayOZQEFx0ZovObNGZLU5m28zdMVMBwD9e4DSJGPsDBsd1GA</recordid><startdate>20241115</startdate><enddate>20241115</enddate><creator>Tang, Wei</creator><creator>Huang, Anmin</creator><creator>Liu, Enbo</creator><creator>Wu, Jiale</creator><creator>Zhang, Renyuan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</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><orcidid>https://orcid.org/0009-0003-8670-6943</orcidid><orcidid>https://orcid.org/0000-0002-7381-5046</orcidid><orcidid>https://orcid.org/0000-0002-4142-5166</orcidid><orcidid>https://orcid.org/0009-0007-7486-0598</orcidid></search><sort><creationdate>20241115</creationdate><title>A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy</title><author>Tang, Wei ; Huang, Anmin ; Liu, Enbo ; Wu, Jiale ; Zhang, Renyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c176t-299d119e19bb3b238a4cd4ed05e7c147ccd0dfc2b7d848fbb27a58efbdf662d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Autonomous navigation</topic><topic>Global navigation satellite system</topic><topic>Global navigation satellite system (GNSS)</topic><topic>Laser modes</topic><topic>Laser radar</topic><topic>Lasers</topic><topic>LiDAR</topic><topic>Localization</topic><topic>Localization method</topic><topic>Location awareness</topic><topic>Measurement by laser beam</topic><topic>Normal distribution</topic><topic>Occlusion</topic><topic>outdoor mobile robot</topic><topic>Point cloud compression</topic><topic>Reliability</topic><topic>robot localization</topic><topic>Robot sensing systems</topic><topic>Robots</topic><topic>sensor fusion</topic><topic>Sensors</topic><topic>Statistical analysis</topic><topic>Three dimensional models</topic><topic>Three-dimensional displays</topic><topic>Urban environments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tang, Wei</creatorcontrib><creatorcontrib>Huang, Anmin</creatorcontrib><creatorcontrib>Liu, Enbo</creatorcontrib><creatorcontrib>Wu, Jiale</creatorcontrib><creatorcontrib>Zhang, Renyuan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</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><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tang, Wei</au><au>Huang, Anmin</au><au>Liu, Enbo</au><au>Wu, Jiale</au><au>Zhang, Renyuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2024-11-15</date><risdate>2024</risdate><volume>24</volume><issue>22</issue><spage>37846</spage><epage>37858</epage><pages>37846-37858</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>Accurate localization is essential for robot autonomous navigation. The localization methods that rely overly on the global navigation satellite system (GNSS) are not reliable in urban environments where GNSS signals are vulnerable to occlusion. In this work, we fuse data from IMU, LiDAR, and GNSS with a particle filter, presenting a novel method based on a two-step particle adjustment strategy. Our algorithm first uses GNSS data to evaluate the current particles and adjust their distribution if necessary. Subsequently, we use laser measurements to evaluate old particles and the reliability of the GNSS data, adjusting the particle distribution for correction. In addition, we use statistical features of point clouds for laser measurements, which transform the global map into a series of normal distribution models, and use these models to match with 3-D laser scans for particle state evaluation. Our method improves the processing efficiency of 3-D point cloud data and fully utilizes its 3-D features during localization. Experimental results demonstrate that our algorithm achieves higher localization accuracy on the publicly available KITTI dataset and in real campus environments. In addition, our algorithm consistently delivers precise localization in both open areas and GNSS-unavailable scenarios, showcasing superior reliability.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2024.3472470</doi><tpages>13</tpages><orcidid>https://orcid.org/0009-0003-8670-6943</orcidid><orcidid>https://orcid.org/0000-0002-7381-5046</orcidid><orcidid>https://orcid.org/0000-0002-4142-5166</orcidid><orcidid>https://orcid.org/0009-0007-7486-0598</orcidid></addata></record> |
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subjects | Algorithms Autonomous navigation Global navigation satellite system Global navigation satellite system (GNSS) Laser modes Laser radar Lasers LiDAR Localization Localization method Location awareness Measurement by laser beam Normal distribution Occlusion outdoor mobile robot Point cloud compression Reliability robot localization Robot sensing systems Robots sensor fusion Sensors Statistical analysis Three dimensional models Three-dimensional displays Urban environments |
title | A Reliable Robot Localization Method Using LiDAR and GNSS Fusion Based on a Two-Step Particle Adjustment Strategy |
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