A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway Scenario

It is important to improve the localization accuracy of the unmanned surface vehicle (USV) for ensuring safe navigation in an inland waterway scenario. However, the localization accuracy of the USV is affected by the limited availability of global navigation satellite system signals, the sparsity of...

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Main Authors: Yang Wang, Chao Liu, Jiahe Liu, Jinzhe Wang, Jianbin Liu, Kai Zheng, Rencheng Zheng
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Journal of Marine Science and Engineering
Subjects:
Online Access:https://www.mdpi.com/2077-1312/12/12/2311
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_version_ 1850242093608337408
author Yang Wang
Chao Liu
Jiahe Liu
Jinzhe Wang
Jianbin Liu
Kai Zheng
Rencheng Zheng
author_facet Yang Wang
Chao Liu
Jiahe Liu
Jinzhe Wang
Jianbin Liu
Kai Zheng
Rencheng Zheng
author_sort Yang Wang
collection DOAJ
description It is important to improve the localization accuracy of the unmanned surface vehicle (USV) for ensuring safe navigation in an inland waterway scenario. However, the localization accuracy of the USV is affected by the limited availability of global navigation satellite system signals, the sparsity of feature points, and the high scene similarity in inland waterway scenarios. Therefore, this paper proposes a laser-based simultaneous localization and mapping (SLAM) algorithm for accurate localization and mapping in inland waterway scenarios. Inertial measurement unit (IMU) data are integrated with lidar data to address motion distortion caused by the frequent motion of the USV. Subsequently, a generalized iterative closest point (GICP) algorithm incorporating rejection sampling is integrated to enhance the accuracy of point cloud matching, involving a two-phase filtering process to select key feature points for matching. Additionally, a mixed global descriptor is constructed by combining point cloud intensity and distance information to improve the accuracy of loop closure detection. Experiments are conducted on the USV-Inland datasets to evaluate the performance of the proposed algorithm. The experimental results show that the proposed algorithm generates accurate mapping and significantly improves localization accuracy by 25.6%, 18.5%, and 23.6% compared to A-LOAM, LeGO-LOAM, and ISC-LOAM, respectively. These results demonstrate that the proposed algorithm achieves accurate localization and mapping in an inland waterway scenario.
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spelling doaj-art-3b10ebc73b38465fbbfeee38df61f7942025-08-20T02:00:23ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212231110.3390/jmse12122311A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway ScenarioYang Wang0Chao Liu1Jiahe Liu2Jinzhe Wang3Jianbin Liu4Kai Zheng5Rencheng Zheng6School of Mechanical Engineering, Tianjin University, Tianjin 300354, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300354, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300354, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300354, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300354, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116086, ChinaSchool of Mechanical Engineering, Tianjin University, Tianjin 300354, ChinaIt is important to improve the localization accuracy of the unmanned surface vehicle (USV) for ensuring safe navigation in an inland waterway scenario. However, the localization accuracy of the USV is affected by the limited availability of global navigation satellite system signals, the sparsity of feature points, and the high scene similarity in inland waterway scenarios. Therefore, this paper proposes a laser-based simultaneous localization and mapping (SLAM) algorithm for accurate localization and mapping in inland waterway scenarios. Inertial measurement unit (IMU) data are integrated with lidar data to address motion distortion caused by the frequent motion of the USV. Subsequently, a generalized iterative closest point (GICP) algorithm incorporating rejection sampling is integrated to enhance the accuracy of point cloud matching, involving a two-phase filtering process to select key feature points for matching. Additionally, a mixed global descriptor is constructed by combining point cloud intensity and distance information to improve the accuracy of loop closure detection. Experiments are conducted on the USV-Inland datasets to evaluate the performance of the proposed algorithm. The experimental results show that the proposed algorithm generates accurate mapping and significantly improves localization accuracy by 25.6%, 18.5%, and 23.6% compared to A-LOAM, LeGO-LOAM, and ISC-LOAM, respectively. These results demonstrate that the proposed algorithm achieves accurate localization and mapping in an inland waterway scenario.https://www.mdpi.com/2077-1312/12/12/2311unmanned surface vehicleinland waterwaylaser-based SLAMrejection samplingloop closure detectionmixed global descriptor
spellingShingle Yang Wang
Chao Liu
Jiahe Liu
Jinzhe Wang
Jianbin Liu
Kai Zheng
Rencheng Zheng
A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway Scenario
Journal of Marine Science and Engineering
unmanned surface vehicle
inland waterway
laser-based SLAM
rejection sampling
loop closure detection
mixed global descriptor
title A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway Scenario
title_full A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway Scenario
title_fullStr A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway Scenario
title_full_unstemmed A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway Scenario
title_short A Laser-Based SLAM Algorithm of the Unmanned Surface Vehicle for Accurate Localization and Mapping in an Inland Waterway Scenario
title_sort laser based slam algorithm of the unmanned surface vehicle for accurate localization and mapping in an inland waterway scenario
topic unmanned surface vehicle
inland waterway
laser-based SLAM
rejection sampling
loop closure detection
mixed global descriptor
url https://www.mdpi.com/2077-1312/12/12/2311
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