用预计算的表面点加速激光惯性里程计，提升速度同时保持精度。
Surfel-LIO: Fast LiDAR-Inertial Odometry with Pre-computed Surfels and Hierarchical Z-order Voxel Hashing
- 采用分层体素与预计算表面点，实现无需搜索的快速匹配
- 在M3DGR数据集上速度远超现有方法，精度相当
- 适合需要实时高帧率定位的自动驾驶与机器人场景
激光惯性里程计（LIO）在无卫星信号环境下可实现高精度实时状态估计，是当前研究热点。尽管地图数据结构和空间索引技术的进步显著提升了效率，但仍有改进空间：一是最近邻搜索需遍历多个空间单元以获取足够点进行平面拟合；二是平面参数通常每迭代一次都重新计算，即使地图几何未变。为此，我们提出Surfel-LIO，采用分层体素结构（hVox）与预计算表面点表示。该设计实现了无需运行时邻居枚举或平面拟合的O(1)对应关系检索，并结合Z-order曲线编码实现缓存友好的空间索引。在M3DGR数据集上的实验表明，本方法相比近期最先进方法处理速度显著更快，同时保持相当的状态估计精度。代码已公开于https://github.com/93won/lidar_inertial_odometry。
原文摘要 · Abstract (English)
LiDAR-inertial odometry (LIO) is an active research area, as it enables accurate real-time state estimation in GPS-denied environments. Recent advances in map data structures and spatial indexing have significantly improved the efficiency of LIO systems. Nevertheless, we observe that two aspects may still leave room for improvement: (1) nearest neighbor search often requires examining multiple spatial units to gather sufficient points for plane fitting, and (2) plane parameters are typically recomputed at every iteration despite unchanged map geometry. Motivated by these observations, we propose Surfel-LIO, which employs a hierarchical voxel structure (hVox) with pre-computed surfel representation. This design enables O(1) correspondence retrieval without runtime neighbor enumeration or plane fitting, combined with Z-order curve encoding for cache-friendly spatial indexing. Experimental results on the M3DGR dataset demonstrate that our method achieves significantly faster processing speed compared to recent state-of-the-art methods while maintaining comparable state estimation accuracy. Our implementation is publicly available at https://github.com/93won/lidar_inertial_odometry.
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