To improve the localization accuracy of the LIO-SAM algorithm, the LiDAR-IMU external parameter calibration is studied in this article. To address the low calibration accuracy of existing sensor calibration algorithms in vehicle-mounted conditions, a new joint calibration algorithm is proposed for vehicle sensors. Due to the low degree of freedom under vehicle conditions, the constraints of pitch and roll direction are not established sufficiently. To solve this problem, we first eliminate the influence of translation parameters by using a wide range of vehicle trajectories. Then, the normal distributions transform and iterative closest point algorithm are used to quickly obtain the initial values of rotation parameters. Furthermore, the calibration accuracy of pitch angle and roll angle is improved. In the coarse calibration process, the LiDAR odometer drifts and translation external parameters are not calibrated. Therefore, we further implement the full parameter calibration scheme based on the point cloud optimization method and make some enhancements. In this scheme, the turning region is utilized to construct constraints on the translation external parameters. Then, we combine the statistical error average effect and the displacement constraint to construct a new objective function. Finally, the full parameter calibration results are obtained by iterative optimization. Compared with the original LIO-SAM algorithm, experimental results show that the localization accuracy of LIO-SAM algorithm with external parameter calibration module is improved by 1. 74% ~ 5. 92% .