Real-time Drivable Region Planning Based on 3D LiDAR

Abstract

Stable, real-time, drivable area planning in a dynamic environment is an essential feature of autonomous vehicles. This paper presents an efficient 3D laser radar-based drivable area planning algorithm. In order to extract the drivable area, the original point cloud data is first downsampled to obtain a relatively sparse point cloud to reduce the complexity. Then, based on the geometric features of the pavement point and dividing the road plane, the obstacle point after the road plane is divided. The cloud is transformed into a 2D aerial view, and a series of expansion, convolution and other regional operations are performed on the bird's-eye view, and the road edge points are extracted, and the curve fitting is performed based on the least squares method to plan the drivable region. The algorithm proposed in this paper was tested on the KITTI dataset and obtained robust, high-efficiency experimental results.