Application of Mapping Algorithm Based on Point Cloud Intensity Characteristics to Map Construction in Rail Transit Scenarios
Carrier localization is one of key technologies in the field of autonomous driving, among which map-based localization techniques have the advantages of high accuracy and robustness. Simultaneous localization and mapping (SLAM) technology serves as a typical method for map construction in unknown en...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2024-08-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.04.009 |
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| Summary: | Carrier localization is one of key technologies in the field of autonomous driving, among which map-based localization techniques have the advantages of high accuracy and robustness. Simultaneous localization and mapping (SLAM) technology serves as a typical method for map construction in unknown environments. However, in metro tunnel scenarios, the traditional SLAM algorithm often results in severe degradation in geometric structures, leading to unsuccessful map construction. To solve this issue, this paper proposes a mapping algorithm based on the intensity characteristics of point clouds. Firstly, feature point clouds were extracted based on point cloud intensity. Moreover, a generalized iterative closest point matching algorithm was introduced to construct residuals for high-intensity feature point clouds, thereby adding motion constraints. Secondly, pose graph optimization was fused with LiDAR data and IMU data to enable pose optimization and map construction. Finally, the offline data collected from real metro tunnels were used to verify the effect of the proposed algorithm, resulting in the successful construction of point cloud maps that cover entire metro lines, without significant drift. Map accuracy was evaluated using identifying objects at fixed installation intervals on the tunnel walls, demonstrating the algorithm's effectiveness and robustness, with an average deviation in maps of less than 0.2 m. |
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| ISSN: | 2096-5427 |