无需调参的激光雷达里程计，能自动识别隧道等退化环境并抑制漂移。
LF-GICP: Parameter-Free Degeneracy-Aware LiDAR Odometry via a Voxel-Normal Localizability Field
- 用体素法构建无正则化的局部可定位场，自动捕捉环境退化特征。
- 在KITTI上相对位移误差仅0.865%，优于所有重跑基线模型。
- 适用于多种传感器与场景，无需重新调参，适合工业部署。
扫描匹配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产生无界漂移，现有方法需针对环境手动调参。本文提出一种无需参数调整的方法：通过体素化GICP中的高斯-牛顿海森矩阵发现，协方差正则化会使平移块保持良好条件性，从而掩盖退化。为此，我们引入无正则化的体素-法向可定位场及其两个统计量——归一化比例 $f_0$ 检测方向各向异性，单体素质量 $λ_0$ 区分信息缺失（隧道）与信息稀释（密集开放场景）。通过时序中值门融合两者，触发费舍尔信息对应权重。经两段短序列一次校准后冻结参数，LF-GICP 在相同评估协议下实现最低的 KITTI 相对位移误差（0.865%），在 GEODE 隧道和 MulRan 上表现更优，领跑 HeLiPR 平均指标，并在四种传感器类型间无需调参即可泛化。实验证明，纯激光雷达注册下，笔直均匀隧道在其轴向上始终不可观测。
原文摘要 · Abstract (English)
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.
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