LF-GICP：基于体素-法向量可定位性场的无参数退化感知激光雷达里程计
LF-GICP: Parameter-Free Degeneracy-Aware LiDAR Odometry via a Voxel-Normal Localizability Field
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中文总结 AI 辅助
本文提出无参数退化感知激光雷达里程计LF-GICP，通过体素-法向量可定位性场解决退化环境漂移问题，在多数据集上精度领先且可跨传感器泛化。
中文摘要 AI 辅助
扫描-地图式激光雷达里程计在隧道、走廊等几何退化环境中，会沿不可观测轴无限制漂移，现有退化处理方法需针对特定环境调整参数。本文提出一种无参数方法：研究发现，在体素化GICP中，高斯-牛顿（GN）海森矩阵因协方差正则化使平移块人为保持良好条件，从而掩盖了平移退化；本文通过无正则化的体素-法向量可定位性场及其两个统计量绕过该问题：归一化分数f₀用于检测方向各向异性，绝对体素质量λ₀用于区分信息缺失（隧道）与信息稀释（密集开放场景）；采用时间中值门限结合两者，触发费希尔信息对应权重。LF-GICP通过固定规则在两个短序列上校准一次后冻结，在相同评估协议下，相较于重新运行的基线方法，取得最低KITTI相对平移误差（0.865%），在GEODE隧道和MulRan数据集上表现更优，在HeLiPR数据集上取得均值领先，且无需重新调整即可泛化至四种传感器类型；本文还通过实验证实，纯激光雷达配准无法观测笔直均匀隧道的轴线方向。
英文摘要
Scan-to-map LiDAR odometry drifts unboundedly along the unobservable axes of geometrically degenerate environments like tunnels and corridors, and existing degeneracy handling requires environment-specific parameter tuning. This paper presents a parameter-free approach. We show that in voxelized GICP the Gauss--Newton (GN) Hessian masks translational degeneracy, because covariance regularization keeps the translation block artificially well-conditioned. We bypass this with a regularization-free voxel-normal localizability field and two of its statistics: a normalized fraction $f_0$ detecting directional anisotropy, and an absolute per-voxel mass $λ_0$ distinguishing information absence (tunnels) from dilution (dense open scenes). A temporal-median gate combines both to trigger Fisher-information correspondence weighting. Calibrated once by fixed rules on two short sequences and then frozen, LF-GICP achieves the lowest KITTI relative translation error ($0.865\%$) under an identical evaluation protocol against re-run baselines, outperforms them on GEODE tunnels and MulRan, leads the HeLiPR mean, and generalizes across four sensor types without re-tuning. We further demonstrate empirically that straight, uniform tunnels remain unobservable along their axis for LiDAR-only registration.
