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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Gespeichert in:

Autor*in:	Sen Wang [verfasserIn] Xiaohe Chen [verfasserIn] Guanyu Ding [verfasserIn] Yongyao Li [verfasserIn] Wenchang Xu [verfasserIn] Qinglei Zhao [verfasserIn] Yan Gong [verfasserIn] Qi Song [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	LiDAR navigation reflector localization motion compensation reflector matching high-speed movement

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 13, p 4479
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:13, p 4479

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21134479

Katalog-ID:	DOAJ086641417

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allfields	10.3390/s21134479 doi (DE-627)DOAJ086641417 (DE-599)DOAJa2605d8e5c0047b084f8236b156f6fbe DE-627 ger DE-627 rakwb eng TP1-1185 Sen Wang verfasserin aut A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. LiDAR navigation reflector localization motion compensation reflector matching high-speed movement Chemical technology Xiaohe Chen verfasserin aut Guanyu Ding verfasserin aut Yongyao Li verfasserin aut Wenchang Xu verfasserin aut Qinglei Zhao verfasserin aut Yan Gong verfasserin aut Qi Song verfasserin aut In Sensors MDPI AG, 2003 21(2021), 13, p 4479 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:13, p 4479 https://doi.org/10.3390/s21134479 kostenfrei https://doaj.org/article/a2605d8e5c0047b084f8236b156f6fbe kostenfrei https://www.mdpi.com/1424-8220/21/13/4479 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 13, p 4479
spelling	10.3390/s21134479 doi (DE-627)DOAJ086641417 (DE-599)DOAJa2605d8e5c0047b084f8236b156f6fbe DE-627 ger DE-627 rakwb eng TP1-1185 Sen Wang verfasserin aut A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. LiDAR navigation reflector localization motion compensation reflector matching high-speed movement Chemical technology Xiaohe Chen verfasserin aut Guanyu Ding verfasserin aut Yongyao Li verfasserin aut Wenchang Xu verfasserin aut Qinglei Zhao verfasserin aut Yan Gong verfasserin aut Qi Song verfasserin aut In Sensors MDPI AG, 2003 21(2021), 13, p 4479 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:13, p 4479 https://doi.org/10.3390/s21134479 kostenfrei https://doaj.org/article/a2605d8e5c0047b084f8236b156f6fbe kostenfrei https://www.mdpi.com/1424-8220/21/13/4479 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 13, p 4479
allfields_unstemmed	10.3390/s21134479 doi (DE-627)DOAJ086641417 (DE-599)DOAJa2605d8e5c0047b084f8236b156f6fbe DE-627 ger DE-627 rakwb eng TP1-1185 Sen Wang verfasserin aut A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. LiDAR navigation reflector localization motion compensation reflector matching high-speed movement Chemical technology Xiaohe Chen verfasserin aut Guanyu Ding verfasserin aut Yongyao Li verfasserin aut Wenchang Xu verfasserin aut Qinglei Zhao verfasserin aut Yan Gong verfasserin aut Qi Song verfasserin aut In Sensors MDPI AG, 2003 21(2021), 13, p 4479 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:13, p 4479 https://doi.org/10.3390/s21134479 kostenfrei https://doaj.org/article/a2605d8e5c0047b084f8236b156f6fbe kostenfrei https://www.mdpi.com/1424-8220/21/13/4479 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 13, p 4479
allfieldsGer	10.3390/s21134479 doi (DE-627)DOAJ086641417 (DE-599)DOAJa2605d8e5c0047b084f8236b156f6fbe DE-627 ger DE-627 rakwb eng TP1-1185 Sen Wang verfasserin aut A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. LiDAR navigation reflector localization motion compensation reflector matching high-speed movement Chemical technology Xiaohe Chen verfasserin aut Guanyu Ding verfasserin aut Yongyao Li verfasserin aut Wenchang Xu verfasserin aut Qinglei Zhao verfasserin aut Yan Gong verfasserin aut Qi Song verfasserin aut In Sensors MDPI AG, 2003 21(2021), 13, p 4479 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:13, p 4479 https://doi.org/10.3390/s21134479 kostenfrei https://doaj.org/article/a2605d8e5c0047b084f8236b156f6fbe kostenfrei https://www.mdpi.com/1424-8220/21/13/4479 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 13, p 4479
allfieldsSound	10.3390/s21134479 doi (DE-627)DOAJ086641417 (DE-599)DOAJa2605d8e5c0047b084f8236b156f6fbe DE-627 ger DE-627 rakwb eng TP1-1185 Sen Wang verfasserin aut A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. LiDAR navigation reflector localization motion compensation reflector matching high-speed movement Chemical technology Xiaohe Chen verfasserin aut Guanyu Ding verfasserin aut Yongyao Li verfasserin aut Wenchang Xu verfasserin aut Qinglei Zhao verfasserin aut Yan Gong verfasserin aut Qi Song verfasserin aut In Sensors MDPI AG, 2003 21(2021), 13, p 4479 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:21 year:2021 number:13, p 4479 https://doi.org/10.3390/s21134479 kostenfrei https://doaj.org/article/a2605d8e5c0047b084f8236b156f6fbe kostenfrei https://www.mdpi.com/1424-8220/21/13/4479 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2021 13, p 4479
language	English
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authorswithroles_txt_mv	Sen Wang @@aut@@ Xiaohe Chen @@aut@@ Guanyu Ding @@aut@@ Yongyao Li @@aut@@ Wenchang Xu @@aut@@ Qinglei Zhao @@aut@@ Yan Gong @@aut@@ Qi Song @@aut@@
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callnumber-first	T - Technology
author	Sen Wang
spellingShingle	Sen Wang misc TP1-1185 misc LiDAR navigation misc reflector localization misc motion compensation misc reflector matching misc high-speed movement misc Chemical technology A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
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topic_title	TP1-1185 A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks LiDAR navigation reflector localization motion compensation reflector matching high-speed movement
topic	misc TP1-1185 misc LiDAR navigation misc reflector localization misc motion compensation misc reflector matching misc high-speed movement misc Chemical technology
topic_unstemmed	misc TP1-1185 misc LiDAR navigation misc reflector localization misc motion compensation misc reflector matching misc high-speed movement misc Chemical technology
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title	A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
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title_sort	lightweight localization strategy for lidar-guided autonomous robots with artificial landmarks
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title_auth	A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks
abstract	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.
abstractGer	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.
abstract_unstemmed	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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author2	Xiaohe Chen Guanyu Ding Yongyao Li Wenchang Xu Qinglei Zhao Yan Gong Qi Song
author2Str	Xiaohe Chen Guanyu Ding Yongyao Li Wenchang Xu Qinglei Zhao Yan Gong Qi Song
ppnlink	331640910
callnumber-subject	TP - Chemical Technology
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isOA_txt	true
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doi_str	10.3390/s21134479
callnumber-a	TP1-1185
up_date	2024-07-03T21:54:56.615Z
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A Lightweight Localization Strategy for LiDAR-Guided Autonomous Robots with Artificial Landmarks

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