Obstacle detection and tracking technology is an important technology in the process of mobile robot driving, which is conducive to improving the movement safety of mobile robots. In order to improve the accuracy of obstacle detection, two improvements have been made to overcome the over-segmentation and under-segmentation of Euclidean clustering. A dynamic Euclidean clustering search radius method is proposed to solve the problem of too sparse distant point clouds, and a method of changing radius search to extended search in the depth direction is proposed to solve the problems of incomplete detection and trailing in the depth direction of point cloud data. In order to improve the accuracy of dynamic obstacle tracking, a new calculation formula of association matrix is designed when two frame obstacle data association is performed, and six degrees of freedom information and size information of the obstacle are added, which improves the success rate of dynamic matching. Simulation experiments show that the improved obstacle detection accuracy reaches 95.2%, and the multi-target tracking accuracy reaches 13.2 mm.