{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T21:11:32Z","timestamp":1783285892455,"version":"3.54.6"},"reference-count":33,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"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":["61975228"],"award-info":[{"award-number":["61975228"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes and implements a lightweight, \u201creal-time\u201d localization system (SORLA) with artificial landmarks (reflectors), which only uses LiDAR data for the laser odometer compensation in the case of high-speed or sharp-turning. Theoretically, due to the feature-matching mechanism of the LiDAR, locations of multiple reflectors and the reflector layout are not limited by geometrical relation. A series of algorithms is implemented to find and track the features of the environment, such as the reflector localization method, the motion compensation technique, and the reflector matching optimization algorithm. The reflector extraction algorithm is used to identify the reflector candidates and estimates the precise center locations of the reflectors from 2D LiDAR data. The motion compensation algorithm predicts the potential velocity, location, and angle of the robot without odometer errors. Finally, the matching optimization algorithm searches the reflector combinations for the best matching score, which ensures that the correct reflector combination could be found during the high-speed movement and fast turning. All those mechanisms guarantee the algorithm\u2019s precision and robustness in the high speed and noisy background. Our experimental results show that the SORLA algorithm has an average localization error of 6.45 mm at a speed of 0.4 m\/s, and 9.87 mm at 4.2 m\/s, and still works well with the angular velocity of 1.4 rad\/s at a sharp turn. The recovery mechanism in the algorithm could handle the failure cases of reflector occlusion, and the long-term stability test of 72 h firmly proves the algorithm\u2019s robustness. This work shows that the strategy used in the SORLA algorithm is feasible for industry-level navigation with high precision and a promising alternative solution for SLAM.<\/jats:p>","DOI":"10.3390\/s21134479","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T02:44:39Z","timestamp":1625107479000},"page":"4479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks"],"prefix":"10.3390","volume":"21","author":[{"given":"Sen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohe","family":"Chen","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanyu","family":"Ding","sequence":"additional","affiliation":[{"name":"Pilot AI Company, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongyao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenchang","family":"Xu","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinglei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Gong","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3078-3363","authenticated-orcid":false,"given":"Qi","family":"Song","sequence":"additional","affiliation":[{"name":"Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China"},{"name":"Pilot AI Company, Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1109\/TIE.2009.2013690","article-title":"Autonomous mobile robot navigation using passive RFID in indoor environment","volume":"56","author":"Park","year":"2009","journal-title":"IEEE Trans. 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