How to Build a Curb Dataset with LiDAR Data for Autonomous Driving
This work tackles the problem of data scarcity for curb detection in autonomous driving systems, which is an incremental step in enhancing road structure perception.
The paper addresses the lack of labeled data for curb detection in autonomous driving by proposing a method to build a curb dataset using LiDAR data, which is essential for improving motion planning in complex urban environments.
Curbs are one of the essential elements of urban and highway traffic environments. Robust curb detection provides road structure information for motion planning in an autonomous driving system. Commonly, video cameras and 3D LiDARs are mounted on autonomous vehicles for curb detection. However, camera-based methods suffer from challenging illumination conditions. During the long period of time before wide application of Deep Neural Network (DNN) with point clouds, LiDAR-based curb detection methods are based on hand-crafted features, which suffer from poor detection in some complex scenes. Recently, DNN-based dynamic object detection using LiDAR data has become prevalent, while few works pay attention to curb detection with a DNN approach due to lack of labeled data. A dataset with curb annotations or an efficient curb labeling approach, hence, is of high demand...