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.

本研究提出了一种基于体素的平面提取方法，结合 PCA 法实现点云分割并采用平面合并方法防止最后优化时所需时间过长，实验表明该方法比其他平面提取方法具有更高的精度和更少的时间成本。