Abstract: In this paper,an obstacle detection algorithm based on the fusion of lidar and camera is proposed to address the poor adaptability of single sensor detection environment in the Formula Student Autonomous China (FSAC).Firstly,the laser point cloud is filtered,de-ground and conditional Euclidean clustering to determine the position of the cone barrel; secondly,the YOLOv7 algorithm is employed to detect the image and obtain the color information; finally,the sensor is spatiotemporal aligned,and the second nearest neighbor algorithm is used for matching to obtain the position and color information of cone barrel obstacles.Using FSAC car as the experimental platform,in the dynamic test,compared with the intersection fusion algorithm,the accuracy of the algorithm is improved by 5.52%,the error reduced by 29.47%,and the speed increased by 21.66%.The improved data well meet the accuracy and real-time requirements of detection,better realize the perception task of unmanned cars,and provide a reference for the fusion perception of unmanned vehicles.