As a crucial infrastructure of intelligent mobile robots, LiDAR-Inertial odometry (LIO) provides the basic capability of state estimation by tracking LiDAR scans. The high-accuracy tracking generally involves the kNN search, which is used with minimizing point-to-plane distance. The cost for this, however, is maintaining a large local map and performing kNN plane fit for each point. In this work, we reduce both time and space complexity of LIO by saving these unnecessary costs. Technically, we design a plane pre-fitting (PPF) pipeline to track the basic skeleton of the 3D scene. In PPF, planes are not fitted individually for each scan, much less for each point, but are updated incrementally as the agent moves. Unlike kNN, the PPF is more robust to noisy and non-strict planes with our iterative Principal Component Analyse (iPCA) refinement. Moreover, a simple yet effective sandwich layer is introduced to eliminate false point-to-plane matches. Our method was extensively tested on a total number of 22 sequences across 5 open datasets, and evaluated in 3 existing state-of-the-art LIO systems. By contrast, our PPF downsizes the local map by at most 64%, achieving up to 3x faster in residual calculating, 1.92x overall FPS, and still keeps the same level of accuracy. We fully open source our implementation at https://github.com/xingyuuchen/LIO-PPF.

翻译：作为智能移动机器人的关键基础设施，激光-惯性里程计（LIO）通过追踪激光扫描点云提供基础状态估计能力。高精度追踪通常涉及kNN搜索，并结合点到平面距离最小化使用。但这一方法的代价在于需要维护大规模局部地图并对每个点进行kNN平面拟合。本研究通过消除这些非必要开销，降低了LIO的时间和空间复杂度。在技术层面，我们设计了平面预拟合（PPF）流水线来追踪三维场景的基本骨架。在PPF中，平面并非针对每次扫描（更非每个点）进行独立拟合，而是随着智能体移动增量式更新。与kNN不同，PPF通过迭代主成分分析（iPCA）精化对含噪及非严格平面具有更强鲁棒性。此外，我们引入一种简单有效的夹层结构来消除虚假的点-平面匹配。该方法在5个公开数据集的22个序列上进行了全面测试，并在3个现有最先进LIO系统中进行了评估。对比结果表明，我们的PPF将局部地图规模缩减最多64%，残差计算速度提升达3倍，整体帧率提升1.92倍，同时保持相同精度水平。我们已在 https://github.com/xingyuuchen/LIO-PPF 完全开源了实现代码。