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A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks

This paper proposes and implements a lightweight, “real-time” 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 LiD...

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Detalles Bibliográficos
Autores principales: Wang, Sen, Chen, Xiaohe, Ding, Guanyu, Li, Yongyao, Xu, Wenchang, Zhao, Qinglei, Gong, Yan, Song, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271365/
https://www.ncbi.nlm.nih.gov/pubmed/34208935
http://dx.doi.org/10.3390/s21134479
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author Wang, Sen
Chen, Xiaohe
Ding, Guanyu
Li, Yongyao
Xu, Wenchang
Zhao, Qinglei
Gong, Yan
Song, Qi
author_facet Wang, Sen
Chen, Xiaohe
Ding, Guanyu
Li, Yongyao
Xu, Wenchang
Zhao, Qinglei
Gong, Yan
Song, Qi
author_sort Wang, Sen
collection PubMed
description This paper proposes and implements a lightweight, “real-time” 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’s 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’s 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.
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spelling pubmed-82713652021-07-11 A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks Wang, Sen Chen, Xiaohe Ding, Guanyu Li, Yongyao Xu, Wenchang Zhao, Qinglei Gong, Yan Song, Qi Sensors (Basel) Article This paper proposes and implements a lightweight, “real-time” 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’s 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’s 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. MDPI 2021-06-30 /pmc/articles/PMC8271365/ /pubmed/34208935 http://dx.doi.org/10.3390/s21134479 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Sen
Chen, Xiaohe
Ding, Guanyu
Li, Yongyao
Xu, Wenchang
Zhao, Qinglei
Gong, Yan
Song, Qi
A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
title A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
title_full A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
title_fullStr A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
title_full_unstemmed A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
title_short A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
title_sort lightweight localization strategy for lidar-guided autonomous robots with artificial landmarks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271365/
https://www.ncbi.nlm.nih.gov/pubmed/34208935
http://dx.doi.org/10.3390/s21134479
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