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 the 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, let alone for each point, but are updated incrementally as the scene 'flows'. 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, LIO-PPF can consume only 36% of the original local map size to achieve up to 4x faster residual computing and 1.92x overall FPS, while maintaining 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，激光雷达-惯性里程计（LIO）通过追踪激光雷达扫描提供状态估计的基本能力。高精度追踪通常涉及k最近邻（kNN）搜索，并配合点平面距离最小化使用。然而，这一代价在于需要维护大型局部地图并对每个点进行kNN平面拟合。本文通过消减这些不必要的计算开销，同时降低LIO的时间复杂度和空间复杂度。在技术层面，我们设计了一种平面预拟合（PPF）流水线，用于追踪三维场景的基本骨架。在PPF中，平面并非针对每次扫描独立拟合（更不必说针对每个点），而是随着场景'流动'进行增量更新。与基于kNN的方法不同，通过我们提出的迭代主成分分析（iPCA）精炼算法，PPF对含噪声和非严格平面具有更强的鲁棒性。此外，我们引入了一种简单有效的夹层结构来消除错误的点-平面对应匹配。我们的方法在5个公开数据集的22个序列上进行了广泛测试，并在3个现有最优LIO系统中进行评估。对比结果表明，LIO-PPF在保持同等精度水平的情况下，仅消耗原始局部地图尺寸的36%，即可实现最高提升4倍的残差计算速度与1.92倍的整体帧率。我们在https://github.com/xingyuuchen/LIO-PPF 完全开源了实现代码。