{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:21:21Z","timestamp":1767183681631,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51309058"],"award-info":[{"award-number":["51309058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science Foundation of Heilongjiang Province under Grant","award":["E2017015"],"award-info":[{"award-number":["E2017015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.<\/jats:p>","DOI":"10.3390\/rs13152864","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T22:35:31Z","timestamp":1626993331000},"page":"2864","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["LiDAR Odometry and Mapping Based on Semantic Information for Outdoor Environment"],"prefix":"10.3390","volume":"13","author":[{"given":"Shitong","family":"Du","sequence":"first","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Yifan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Xuyou","family":"Li","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Menghao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. 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