Road Curb Detection Using A Novel Tensor Voting Algorithm
This addresses safety and robustness in autonomous navigation, but appears incremental as it builds on existing tensor voting methods with specific adaptations.
The paper tackles road curb detection for autonomous driving by proposing a novel tensor voting algorithm that processes 3D LiDAR point clouds, achieving near real-time performance.
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.