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.

翻译：然而,由于LiDAR点云的密度分布不同,使用现有构成估计方法实现基于 3D LiDAR 的精确方向提取具有挑战性。在本文中,我们提议了一种新型的光柱船体型车辆构成估计方法。提取的3D集群将缩小为光柱体,减少计算负担。然后为基于搜索的算法开发了基于最小隔离区的新标准,从而可以实现准确的构成估计。拟议的算法在KITTI数据集和在工业园区获得的人工标签数据集上得到验证。结果显示,我们拟议的方法在保持实时速度的同时,可以比三种主流算法更准确。