LIO-PPF：通过增量平面预拟合与骨架追踪实现的快速LiDAR-惯性里程计
LIO-PPF: Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking
AI总结：
提出LIO-PPF方法，通过增量平面预拟合（PPF）与iPCA细化追踪3D场景骨架，并引入夹层层消除错误匹配，显著降低LiDAR-惯性里程计的时空复杂度，在保持精度的同时大幅提升速度。
摘要：
作为智能移动机器人的关键基础设施，LiDAR-惯性里程计（LIO）通过追踪LiDAR扫描提供状态估计的基本能力。高精度追踪通常涉及kNN搜索，并与最小化点到平面距离结合使用。然而，其代价是维护一个大型局部地图并对每个点执行kNN平面拟合。在本研究中，我们通过节省这些不必要的开销，降低了LIO的时间和空间复杂度。在技术上，我们设计了一个平面预拟合（PPF）流水线来追踪3D场景的基本骨架。在PPF中，平面不是为每次扫描单独拟合的，更不是为每个点单独拟合的，而是随着场景“流动”增量更新的。与kNN不同，通过我们的迭代主成分分析（iPCA）细化，PPF对噪声和非严格平面更具鲁棒性。此外，引入了一个简单而有效的夹层（sandwich）层来消除错误的点到平面匹配。我们的方法在5个开放数据集上的共22个序列中进行了广泛测试，并在3个现有的最先进LIO系统中进行了评估。相比之下，LIO-PPF仅需消耗原始局部地图大小的36%，即可实现高达4倍的残差计算速度和1.92倍的总体FPS，同时保持相同的精度水平。我们在https://github.com/xingyuuchen/LIO-PPF完全开源了我们的实现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
