{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:52:56Z","timestamp":1763664776939,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China National key research and development plan","award":["2020YFC1512001"],"award-info":[{"award-number":["2020YFC1512001"]}]},{"name":"China Natural Science Foundation","award":["U1934215, 41901412"],"award-info":[{"award-number":["U1934215, 41901412"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mobile laser scanning (MLS) point cloud registration plays a critical role in mobile 3D mapping and inspection, but conventional point cloud registration methods for terrain LiDAR scanning (TLS) are not suitable for MLS. To cope with this challenge, we use inertial measurement unit (IMU) to assist registration and propose an MLS point cloud registration method based on an inertial trajectory error model. First, we propose an error model of inertial trajectory over a short time period to construct the constraints between trajectory points at different times. On this basis, a relationship between the point cloud registration error and the inertial trajectory error is established, then trajectory error parameters are estimated by minimizing the point cloud registration error using the least squares optimization. Finally, a reliable and concise inertial-assisted MLS registration algorithm is realized. We carried out experiments in three different scenarios: indoor, outdoor and integrated indoor\u2013outdoor. We evaluated the overall performance, accuracy and efficiency of the proposed method. Compared with the ICP method, the accuracy and speed of the proposed method were improved by 2 and 2.8 times, respectively, which verified the effectiveness and reliability of the proposed method. Furthermore, experimental results show the significance of our method in constructing a reliable and scalable mobile 3D mapping system suitable for complex scenes.<\/jats:p>","DOI":"10.3390\/rs14061365","type":"journal-article","created":{"date-parts":[[2022,3,13]],"date-time":"2022-03-13T21:44:17Z","timestamp":1647207857000},"page":"1365","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["IMU-Aided Registration of MLS Point Clouds Using Inertial Trajectory Error Model and Least Squares Optimization"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8537-2409","authenticated-orcid":false,"given":"Zhipeng","family":"Chen","sequence":"first","affiliation":[{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"},{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Qingquan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"},{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9850-1668","authenticated-orcid":false,"given":"Jiayuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Dejin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"},{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9364-1784","authenticated-orcid":false,"given":"Jianwei","family":"Yu","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Yu","family":"Yin","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Shiwang","family":"Lv","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"},{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Anbang","family":"Liang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.isprsjprs.2020.03.013","article-title":"Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark","volume":"163","author":"Dong","year":"2020","journal-title":"ISPRS J. 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