The conventional truck point cloud registration algorithm enables point cloud registration by determining the key features among point clouds. However, this method is inefficient because of the presence of repeated scenes and noise points among point clouds. Furthermore, the key features obtained using this algorithm are often inaccurate. Therefore, herein, a multiview point cloud registration method based on laser radar is proposed. In the proposed method, the inertial measurement unit is introduced into point cloud registration to complete the pose correction of the corresponding point cloud without relying on point cloud data. Then, the random sampling consensus algorithm is used to fit the local plane to determine the nearest point, which is integrated with the nearest point iterative algorithm to rapidly identify the corresponding point set and realize the accurate registration of the point cloud. The proposed method is verified via an experiment on a truck data set. The proposed method can complete the registration within 4 s, the maximum translation error is 0.01 m, and the rotation error is within 0.1°. Experimental results confirm that the proposed method exhibits good registration efficiency and accuracy in truck point cloud registration and shows high applicability.