Fast Obstacle Detection Using 3D-to-2D LiDAR Point Cloud Segmentation for Collision-free Path Planning

Abstract

Whereas many existing computer vision algorithms based on color images work well in robot navigation, most of them are sensitive to illumination and the reflectance of objects. Furthermore, according to the Oren-Nayar reflectance model, the reflectance depends on the material and surface of objects. Therefore, different sensors, passive sensors and active sensors, are used simultaneously to scan objects of different materials. In this paper, an integrated sensor system, including light detection and ranging (LiDAR), global positioning system (GPS), gyroscopes (Gyro), and a camera, is proposed for an autonomous vehicle. The GPS and Gyros are used for locating the robot position and identifying its orientation, which are used for global path planning to move toward a goal. The camera is used for remote video monitoring. LiDAR is used to capture the point clouds of the current environment for use in planning the local path. In this paper, a fast segmentation method is proposed for obstacle detection. The proposed method includes ground point removal, region of interest (ROI) detection, 3D-to-2D projection, and clustering by grids. The purpose of ROI detection is to determine whether the points are candidate obstacle points. The experimental results show that the proposed segmentation method can reduce the size of the point cloud and computation complexity significantly. The integrated multisensor system is expected to be practically used in the field.