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This paper preprocesses the sensor data and presents <jats:bold>HT-Carto<\/jats:bold>, an improved <jats:bold>h<\/jats:bold>ybrid point-cloud filtering system, and a <jats:bold>t<\/jats:bold>ightly coupled LiDAR\/IMU framework based on <jats:bold>Carto<\/jats:bold>grapher\u2019s front-end. The inertial measurement unit (IMU) provides initial values for the point cloud, and the IMU pre-integration combines the scan-matched pose to construct the factors, which are added as constraints to the factor graph. The result is used to update the current pose and work as odometer residuals at the back-end. The optimization of the selected strategy during point cloud preprocessing, PassThrough, and RadiusOutlierRemoval are combined to ensure quality. An actual vehicle is used in complex indoor environment to verify the stability and robustness of HT-Carto. Compared to the Cartographer, Karto, Hector, and GMapping, HT-Carto demonstrates better localization and mapping, it can obtain a more precise trajectory.<\/jats:p>","DOI":"10.1017\/s0263574725000463","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T23:46:50Z","timestamp":1744328810000},"page":"1708-1721","source":"Crossref","is-referenced-by-count":3,"title":["Tightly coupled SLAM system for indoor complex scenes"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6565-8535","authenticated-orcid":false,"given":"Chen","family":"Da","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zailiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianlin","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaping","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"S0263574725000463_ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2006.889486"},{"key":"S0263574725000463_ref26","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2023.3316010"},{"key":"S0263574725000463_ref28","doi-asserted-by":"publisher","DOI":"10.1017\/S026357472300053X"},{"key":"S0263574725000463_ref8","doi-asserted-by":"crossref","unstructured":"[8] Kohlbrecher, S. , von Stryk, O. , Meyer, J. and Klingauf, U. , \u201cA Flexible and Scalable Slam System with Full 3D Motion Estimation,\u201d In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics (2011) pp. 155\u2013160.","DOI":"10.1109\/SSRR.2011.6106777"},{"key":"S0263574725000463_ref17","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3059567"},{"key":"S0263574725000463_ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3338722"},{"key":"S0263574725000463_ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3063477"},{"key":"S0263574725000463_ref11","doi-asserted-by":"crossref","first-page":"18815","DOI":"10.1038\/s41598-022-22938-y","article-title":"Finding the best hardware configuration for 2d slam in indoor environments via simulation based on google cartographer","volume":"12","author":"\u0141ukasz","year":"2022","journal-title":"Sci. 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Robotics: Science and Systems X, University of California, Berkeley, USA, July 12-16, 2014 (2014).","DOI":"10.15607\/RSS.2014.X.007"},{"key":"S0263574725000463_ref6","doi-asserted-by":"crossref","unstructured":"[6] Hess, W. , Kohler, D. , Rapp, H. and Andor, D. , \u201cReal-Time Loop Closure in 2D Lidar Slam,\u201d In: 2016 IEEE International Conference on Robotics and Automation (ICRA) (2016) pp. 1271\u20131278.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"S0263574725000463_ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2014.2311416"},{"key":"S0263574725000463_ref20","doi-asserted-by":"crossref","unstructured":"[20] Shan, T. and Englot, B. , \u201cLego-Loam: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain,\u201d In: 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018) pp. 4758\u20134765.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"S0263574725000463_ref24","first-page":"1","article-title":"A factor graph optimization method for high-precision imu-based navigation system","volume":"72","author":"Lyu","year":"2023","journal-title":"IEEE Trans. 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