Bundle adjustment (BA) on LiDAR point clouds has been extensively investigated in recent years due to its ability to optimize multiple poses together, resulting in high accuracy and global consistency for point cloud. However, the accuracy and speed of LiDAR bundle adjustment depend on the quality of plane extraction, which provides point association for LiDAR BA. In this study, we propose a novel and efficient voxel-based approach for plane extraction that is specially designed to provide point association for LiDAR bundle adjustment. To begin, we partition the space into multiple voxels of a fixed size and then split these root voxels based on whether the points are on the same plane, using an octree structure. We also design a novel plane determination method based on principle component analysis (PCA), which segments the points into four even quarters and compare their minimum eigenvalues with that of the initial point cloud. Finally, we adopt a plane merging method to prevent too many small planes from being in a single voxel, which can increase the optimization time required for BA. Our experimental results on HILTI demonstrate that our approach achieves the best precision and least time cost compared to other plane extraction methods.

翻译：近年来，针对LiDAR点云的束调整（BA）因能同时优化多个位姿、实现点云的高精度与全局一致性而受到广泛研究。然而，LiDAR束调整的精度与速度取决于为其提供点关联的平面提取质量。本研究提出一种新颖且高效的基于体素的平面提取方法，该方法是专为LiDAR束调整提供点关联而设计的。首先，我们将空间划分为多个固定大小的体素，然后基于点是否位于同一平面，采用八叉树结构对这些根体素进行分割。我们还设计了一种基于主成分分析（PCA）的新型平面判定方法：将点云分割为四个均匀的四分之一区域，并将其最小特征值与初始点云的最小特征值进行比较。最后，我们采用平面合并方法，防止单个体素内产生过多小平面，从而避免增加束调整所需的优化时间。在HILTI数据集上的实验结果表明，与其它平面提取方法相比，我们的方法实现了最佳精度和最少时间消耗。