Multiple moving object detection and tracking method using point cloud segmentation

• Main Authors: ZHAO, BO-XU, 趙伯勗
• Other Authors: LIN, CHIEN-CHOU
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/q65u84

碩士 === 國立雲林科技大學 === 資訊工程系 === 106 === In this thesis, a moving detection and tracking method is proposed for multiple targets by using point cloud segmentation. LIDAR systems are widely used in autonomous systems. In an ego-motion system, it is an interesting research topic to identify moving objects from scene point clouds obtained by the mobile LIDAR. The proposed method can detect moving objects within a moving scene and the information of moving objects, e.g., relative velocity, can be used for collision avoidance for a driverless vehicle. The proposed approach consists of five steps: (1) point cloud capturing, (2) ground point removal, (3) segmentation, (4) foreground and background detection, (5) moving object tracking. Firstly, the 3D point cloud scene is retrieved by LiDAR mounted on a ego-motion system. Then, in order to reduce the computation complexity, ground points are removed by the ground detection algorithm. In third step, the rest points are grouped and segmented by the voxel grouping method to eliminate the noise point and to form objects. The velocities of objects are computed with respect to the ego-motion system for identifying the foreground (moving object) and the background (static objects). Finally, Kalman filter is used to track moving objects and to expect the position of these objects. The expecting position of moving objects can be used for collision avoidance.