A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application
Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5877 |
| _version_ | 1797464012743507968 |
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| author | Gang Wang Xiaomeng Wei Yu Chen Tongzhou Zhang Minghui Hou Zhaohan Liu |
| author_facet | Gang Wang Xiaomeng Wei Yu Chen Tongzhou Zhang Minghui Hou Zhaohan Liu |
| author_sort | Gang Wang |
| collection | DOAJ |
| description | Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches. |
| first_indexed | 2024-03-09T18:01:55Z |
| format | Article |
| id | doaj.art-501ef82dde8f468097753912ed7b2e40 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:01:55Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-501ef82dde8f468097753912ed7b2e402023-11-24T09:51:54ZengMDPI AGRemote Sensing2072-42922022-11-011422587710.3390/rs14225877A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its ApplicationGang Wang0Xiaomeng Wei1Yu Chen2Tongzhou Zhang3Minghui Hou4Zhaohan Liu5College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Software, Jilin University, Changchun 130012, ChinaCollege of Software, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Electronic Science and Engineering, Jilin University, Changchun 130012, ChinaSimultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches.https://www.mdpi.com/2072-4292/14/22/5877autonomous drivingunmanned ground vehicleLiDARsimultaneous localization and mappingloop closure detection |
| spellingShingle | Gang Wang Xiaomeng Wei Yu Chen Tongzhou Zhang Minghui Hou Zhaohan Liu A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application Remote Sensing autonomous driving unmanned ground vehicle LiDAR simultaneous localization and mapping loop closure detection |
| title | A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application |
| title_full | A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application |
| title_fullStr | A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application |
| title_full_unstemmed | A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application |
| title_short | A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application |
| title_sort | multi channel descriptor for lidar based loop closure detection and its application |
| topic | autonomous driving unmanned ground vehicle LiDAR simultaneous localization and mapping loop closure detection |
| url | https://www.mdpi.com/2072-4292/14/22/5877 |
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