Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm
Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to...
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MDPI AG
2024-06-01
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| author | Tingchen Ma Lingxin Kong Yongsheng Ou Sheng Xu |
| author_facet | Tingchen Ma Lingxin Kong Yongsheng Ou Sheng Xu |
| author_sort | Tingchen Ma |
| collection | DOAJ |
| description | Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 <inline-formula><math display="inline"><semantics><mrow><mo>×</mo><mspace width="3.33333pt"></mspace><msup><mn>10</mn><mn>3</mn></msup></mrow></semantics></math></inline-formula> order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward–reverse–forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having <inline-formula><math display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mn>1</mn><mo>)</mo></mrow></semantics></math></inline-formula> time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage. |
| format | Article |
| id | doaj-art-89580cd6090d4f47bb56835d61dc05a4 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-89580cd6090d4f47bb56835d61dc05a42025-08-20T02:26:40ZengMDPI AGSensors1424-82202024-06-012412401110.3390/s24124011Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching AlgorithmTingchen Ma0Lingxin Kong1Yongsheng Ou2Sheng Xu3Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaGuangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaGuangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSimultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 <inline-formula><math display="inline"><semantics><mrow><mo>×</mo><mspace width="3.33333pt"></mspace><msup><mn>10</mn><mn>3</mn></msup></mrow></semantics></math></inline-formula> order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward–reverse–forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having <inline-formula><math display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mn>1</mn><mo>)</mo></mrow></semantics></math></inline-formula> time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage.https://www.mdpi.com/1424-8220/24/12/4011hash multi-scale mapbidirectional matchingLiDAR SLAMrobot navigation |
| spellingShingle | Tingchen Ma Lingxin Kong Yongsheng Ou Sheng Xu Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm Sensors hash multi-scale map bidirectional matching LiDAR SLAM robot navigation |
| title | Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm |
| title_full | Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm |
| title_fullStr | Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm |
| title_full_unstemmed | Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm |
| title_short | Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm |
| title_sort | accurate 3d lidar slam system based on hash multi scale map and bidirectional matching algorithm |
| topic | hash multi-scale map bidirectional matching LiDAR SLAM robot navigation |
| url | https://www.mdpi.com/1424-8220/24/12/4011 |
| work_keys_str_mv | AT tingchenma accurate3dlidarslamsystembasedonhashmultiscalemapandbidirectionalmatchingalgorithm AT lingxinkong accurate3dlidarslamsystembasedonhashmultiscalemapandbidirectionalmatchingalgorithm AT yongshengou accurate3dlidarslamsystembasedonhashmultiscalemapandbidirectionalmatchingalgorithm AT shengxu accurate3dlidarslamsystembasedonhashmultiscalemapandbidirectionalmatchingalgorithm |