LiDAR-Only Ground Vehicle Navigation System in Park Environment
Abstract
:1. Introduction
- We developed a map representation and maintenance form based on a multi-layer map architecture, proposed a ground detection method based on regional ground plane fitting and a topology extraction method based on probabilistic road map, and an integrated dynamic objects removal algorithm;
- Based on the hierarchical navigation architecture, we established a three-level planner for path planning and dynamic obstacle avoidance at different distances; The method in this paper is integrated into a real unmanned mobile platform, and the experimental tests are carried out in a complex park environment to verify the effectiveness of the navigation system.
2. Related Work
3. Proposed System
3.1. System Framework
3.2. Mapping Representation Module
3.2.1. Global Raw Point Cloud Map
3.2.2. Traverse Area Detection and Representation
3.2.3. Dynamic Point Cloud Detection and Removal
3.2.4. Topological Skeleton Extraction
3.2.5. Middle Map Construction
3.2.6. Local Map Maintenance
3.3. The Planning Module
3.3.1. Global Planner
3.3.2. Middle Planner
3.3.3. Local Planner
- Free Path: The free path uses the sampled discrete points for forward simulation, which represents the possible movement of the vehicle in the future. As shown in Figure 11, through multiple data uniform interpolation and data fitting, 729 possible paths are planned as candidates;
- Voxel Grid: For collision checking, the local planner uses a voxel grid with Free Path overlaid. Considering the occlusion of the vehicle radius, the voxel grid is generated within a certain range according to the spline distance;
- Collision Detection: After the voxel grid is generated, in order to be more efficient in the screening of local paths, each grid and path index are calculated offline. By finding neighbors within a specified distance around a waypoint in a set of paths that belong to a set of voxel grids. Finally, the index relationship table between each voxel grid and the feasible path are obtained.
- Path Filtering: First, the planner will filter the blocked paths, which can be achieved by using the index relationship table. From the remaining paths, set up a cost function based on the point cloud height between the ground threshold and the obstacle threshold. At the same time, the angle difference between each path and the target point and the angle difference between the direction of the path and the current vehicle orientation are used to calculate the cost.
4. Experiment
4.1. Traverse Area Detection
4.2. Dynamic Detection and Removal
4.3. Planning Results
4.4. Dynamic Obstacle Avodiance
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Wang, K.; Li, J.; Xu, M.; Chen, Z.; Wang, J. LiDAR-Only Ground Vehicle Navigation System in Park Environment. World Electr. Veh. J. 2022, 13, 201. https://doi.org/10.3390/wevj13110201
Wang K, Li J, Xu M, Chen Z, Wang J. LiDAR-Only Ground Vehicle Navigation System in Park Environment. World Electric Vehicle Journal. 2022; 13(11):201. https://doi.org/10.3390/wevj13110201
Chicago/Turabian StyleWang, Kezhi, Jianyu Li, Meng Xu, Zonghai Chen, and Jikai Wang. 2022. "LiDAR-Only Ground Vehicle Navigation System in Park Environment" World Electric Vehicle Journal 13, no. 11: 201. https://doi.org/10.3390/wevj13110201