LiDAR odometry is the task of estimating the ego-motion of the sensor from
sequential laser scans. This problem has been addressed by the community for
more than two decades, and many effective solutions are available nowadays.
Most of these systems implicitly rely on assumptions about the operating
environment, the sensor used, and motion pattern. When these assumptions are
violated, several well-known systems tend to perform poorly. This paper
presents a LiDAR odometry system that can overcome these limitations and
operate well under different operating conditions while achieving performance
comparable with domain-specific methods. Our algorithm follows the well-known
ICP paradigm that leverages a PCA-based kd-tree implementation that is used to
extract structural information about the clouds being registered and to compute
the minimization metric for the alignment. The drift is bound by managing the
local map based on the estimated uncertainty of the tracked pose. To benefit
the community, we release an open-source C++ anytime real-time implementation.

该研究论文介绍了一种 LiDAR 里程计系统，能够在不同操作条件下正常运行，并实现与特定领域方法相当的性能。该系统使用基于 PCA 的 kd 树实现的 ICP 范式，用于提取待配准点云的结构信息并计算对齐的最小化度量。通过根据估计的姿态不确定性管理局部地图，将漂移控制在一定范围内。为了使研究社区受益，我们发布了一个开放源代码的 C++ 实现，可实时应用在任何时候。