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Traditional SLAM methodologies, however, are prone to altitude-axis distortions resulting from cumulative errors. To mitigate these issues, Gravity-Constrained SLAM (GC-SLAM) is introduced as a novel computational method that integrates gravity constraints and incremental optimisation to enhance mapping accuracy and computational efficiency. GC-SLAM incorporates a gravity constraint handling actor within the global optimisation algorithm, effectively reducing vertical-axis errors caused by accumulated drift during mapping. Furthermore, an incremental optimisation strategy is employed to manage the computational complexity associated with increasing map size. Performance evaluations of GC-SLAM are conducted on the KITTI dataset and large-scale environments, comparing its effectiveness against state-of-the-art SLAM-based algorithms, including FAST-LIO2, LIO-SAM (Lidar Inertial Odometry and SLAM), Lego-LOAM (Lightweight and Ground-optimised Lidar Odometry and Mapping), and A-LOAM (Advanced Lidar Odometry and Mapping). Experimental results demonstrate that GC-SLAM effectively suppresses vertical-axis distortions, significantly enhances localisation accuracy, and outperforms competing methods.<\/jats:p>","DOI":"10.1177\/10692509251331372","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T01:58:41Z","timestamp":1744595921000},"page":"229-243","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Gravity-Constrained Simultaneous Localization and Mapping for suppressing map warping in complex large-scale environments"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4493-3598","authenticated-orcid":false,"given":"Kaiyi","family":"Xian","sequence":"first","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3040-2065","authenticated-orcid":false,"given":"Duo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8034-0977","authenticated-orcid":false,"given":"Gexiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6100-6532","authenticated-orcid":false,"given":"Ferrante","family":"Neri","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, University of Surrey, Guildford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9193-5369","authenticated-orcid":false,"given":"Song","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang, China"}]}],"member":"179","published-online":{"date-parts":[[2025,4,13]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2019.2945031"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2019.2963439"},{"key":"e_1_3_4_4_2","doi-asserted-by":"crossref","unstructured":"Zhai Y Fu Y Li X. 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