  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.
