The calibration of exterior parameters of a 3D lidar is the basis of an intelligent vehicle perceiving environment through the lidar. Aiming at the major problems of common calibration methods including cumbersome implementation, low precision, and relying on other sensors, we propose a step-by-step automatic calibration algorithm. The first step is to get the ground equation by fitting the ground point cloud, construct the levelness function, and complete the calibration of the pitch angle, roll angle, and longitudinal displacement of the lidar by optimizing the levelness function with the particle swarm optimization (PSO) algorithm. Based on the completion of the first step calibration, the second step calibration needs to collect multi-frame point clouds containing the same calibration pole during the vehicle moving along a straight line. The calibration pole is clustered to get the clustering centers. The linear equation with multi-frame clustering centers is fitted in the two-dimensional plane, and the yaw angle is calculated according to the slope of the fitted straight line. The results show that the precision of the proposed algorithm can reach the magnitude order of 10-5, and the algorithm takes 0.5 s. The calibration precision and efficiency are greatly improved, and actual engineering demands can be satisfied. The two steps are done automatically by the program, and the calibration results with high-precision can be acquired without other sensors.