Name
Abstract | ||
|---|---|---|
Road curb detection is very important and necessary for autonomous driving because it can improve the safety and robustness of robot navigation in the outdoor environment. In this paper, a novel road curb detection method based on tensor voting is presented. The proposed method processes the dense point cloud acquired using a 3D LiDAR. Firstly, we utilize a sparse tensor voting approach to extract the line and surface features. Then, we use an adaptive height threshold and a surface vector to extract the point clouds of the road curbs. Finally, we utilize the height threshold to segment different obstacles from the occupancy grid map. This also provides an effective way of generating high-definition maps. The experimental results illustrate that our proposed algorithm can detect road curbs with near real-time performance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ROBIO49542.2019.8961544 | 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) |
Keywords | Field | DocType |
tensor voting algorithm,dense point cloud,sparse tensor,surface features,adaptive height threshold,point clouds,road curb detection method,3D LiDAR,high-definition maps | Tensor voting,Algorithm,Robustness (computer science),Lidar,Engineering,Point cloud,Robot,Occupancy grid mapping | Conference |
ISBN | Citations | PageRank |
978-1-7281-6322-2 | 0 | 0.34 |
References | Authors | |
10 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilong Zhu | 1 | 6 | 6.16 |
| Dong Han | 2 | 0 | 0.34 |
| Bohuan Xue | 3 | 0 | 0.34 |
| Jianhao Jiao | 4 | 19 | 6.68 |
| Zuhao Zou | 5 | 0 | 0.34 |
| Ming Liu | 6 | 775 | 94.83 |
| Rui Fan | 7 | 258 | 28.91 |