LiDAR-based Drivable Region Detection for Autonomous Driving

Abstract

For autonomous driving, drivable region detection is one of the most basic and essential tasks. In this paper, a novel LiDAR-based drivable region detection algorithm which could output a complete, accurate and stable result is proposed. To promote the completeness of the detection result, the Bayesian generalized kernel inference and bilateral filtering are utilized to estimate the attribute of those unobserved cells. To ensure the traversability, a region growing operator is performed on the normal vector map which reflects the slope of the terrain, thus closely related to the traversability of the vehicle. To improve the result’s stability, information from multiple frames are fused together in the Kalman Filter framework. Experiments are performed both on public dataset and our own dataset. Experimental results show that the proposed algorithm could run in real-time and outperforms state-of-the-art approaches.