Obstacle Detection Based on Lidar and Machine Vision

Based on lidar and machine vision vehicle camera, the obstacle detection of intelligent vehicle road is studied. Through the analysis of lidar and CMOS vehicle camera, the radar and camera are calibrated. In order to improve the defects of image extraction in low illumination environment, Retinex algorithm is developed to enhance the low illumination image. Because of the different frequency of two sensors, combined with the D-S evidence theory, the vehicle camera and lidar data are fused to accurately identify the pedestrian and vehicle information in the real environment. After fusing the relevant data, it can be found that the detection probability of the system is higher than that of a single sensor. In the verification process, by comparing the start point of obstacle avoidance trajectory and the distance between obstacles before and after data fusion, compared with the distance between obstacles before fusion, the distance between the start point of obstacle avoidance trajectory of the sixth target after data fusion is the smallest, the distance in advance of the fourth target is the largest, verifying that the obstacle detection is more effective and timely after sensor data fusion.