The data fusion between onboard Light Detection and Ranging（LiDAR）and GNSS/IMU can improve the accuracy of the localization system in intelligent driving， and the calibration of external parameters between two sensors is the premise of data fusion. It is hard to measure external parameters between onboard sensors manually and the the calibration of automation degree is lower. In this paper， an automatic calibration method that does not rely on markers is proposed. First， the line feature map and surface feature map are constructed， respectively， and closed-loop constraints are used to reduce the accumulated error. The laser points in each frame are matched with surface feature voxels and line feature clusters extracted from two feature maps. The matching errors are integrated with the constraints based on motion calibration. Then a least squares problem is built to optimize the external parameters. Finally， we evaluate the efficiency and robustness of the proposed algorithm by conducting experiments with real vehicles.