An Efficient Convex Hull-Based Vehicle Pose Estimation Method for 3D LiDAR

Vehicle pose estimation is essential in the perception technology of autonomous driving. However, due to the different density distributions of the LiDAR point cloud, it is challenging to achieve accurate direction extraction based on 3D LiDAR by using the existing pose estimation methods. In this paper, we proposed a novel convex hull-based vehicle pose estimation method. The extracted 3D cluster is reduced to the convex hull, reducing the computation burden. Then a novel criterion based on the minimum occlusion area is developed for the search-based algorithm, which can achieve accurate pose estimation. The proposed algorithm is validated on the KITTI dataset and a manually labeled dataset acquired at an industrial park. The results show that our proposed method can achieve better accuracy than the three mainstream algorithms while maintaining real-time speed.