Deep learning based curb detection with Lidar  

作　　者：WANG Xiaohua LIAO Zhonghe MA Pin MIAO Zhonghua 王小华;LIAO Zhonghe;MA Pin;MIAO Zhonghua(School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,P.R.China)

机构地区：[1]School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,P.R.China

出　　处：《High Technology Letters》2022年第3期272-279,共8页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No.51875331)。

摘　　要：Curb detection provides road boundary information and is important to road detection.However,curb detection is challenging due to the problems such as various curb shapes,colour,discontinuity.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 segmented 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 including intersections.The experimental results validate the effectiveness and robustness of this curb detection method.

关 键 词：curb detection EdgeNet curb annotation algorithm 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U463.67[自动化与计算机技术—计算机科学与技术]