Curb Detection in Urban Traffic Scenarios Using LiDARs Point Cloud and Semantically Segmented Color Images

S.E.C. Deac, I. Giosan, S. Nedevschi

Proceeding of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zeeland, 26-30 October,2019, pp. 3433-3440.

In this paper we propose a robust curb detection method which is based on the fusion between semantically labeled camera images and a 3D point cloud coming from LiDAR sensors. The labels from the semantically enhanced cloud are used to reduce the curbs’ searching area. Several spatial cues are next computed on each candidate curb region. Based on these features, a candidate curb region is either rejected or refined for obtaining a precise positioning of the curb points found inside it. A novel local model-based outlier removal algorithm is proposed to filter out the erroneous curb points. Finally, a temporal integration of the detected curb points in multiple consecutive frames is used to densify the detection result. An objective evaluation of the proposed solution is done using a highresolution digital map containing ground truth curb points. The proposed system has proved capable of detecting curbs of any heights (from 3cm up to 30cm) in complex urban road scenarios (straight roads, curved roads, intersections with traffic isles and roundabouts).