Deep learning based curb detection with Lidar

• Published in: 高技术通讯（英文版） Vol. 28; no. 3; pp. 272 - 279
• Main Authors: WANG Xiaohua, LIAO Zhonghe, MA Pin, MIAO Zhonghua
• Format: Journal Article
• Language: English
• Published: School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,P.R.China 10.09.2022
• Subjects: curb annotation algorithm; EdgeNet; curb detection
• ISSN: 1006-6748
• DOI: 10.3772/j.issn.1006-6748.2022.03.006

Curb detection provides road boundary information and is important to road detection. Howev-er, curb detection is challenging due to the problems such as various curb shapes, colour, disconti-nuity. In this work, a novel learning-based method for curb detection is proposed using Lidar point clouds, considering that Lidars are not sensitive to illumination and are relatively stable to weather conditions. A deep neural network, named EdgeNet, is constructed and trained, which handles point clouds in an end-to-end way. After EdgeNet is properly trained, curb points are then segmen-ted in the neural network output. In order to train, a curb point annotation algorithm is also designed to generate training dataset. The curb detection method works well with different road scenarios in-cluding intersections. The experimental results validate the effectiveness and robustness of this curb detection method.