外观
论文arXiv 2609.171451 个月前
LiLi：基于李理论的3D LiDAR扫描配准退化检测
原文链接：LiLi: Lie Theory Based 3D LiDAR Scan Alignment Degeneracy Detection（arXiv 2609.17145 · 发布日期：2026-09-15）
LiLi: Lie Theory Based 3D LiDAR Scan Alignment Degeneracy Detection
LiLi用李理论识别SE(3)中的退化变换，在合成数据和真实退化数据上提升3D LiDAR配准与里程计定位。
核心要点
- 直走廊、平坦场地等退化场景中，3D LiDAR配准解不唯一，会损害定位与建图精度。
- 现有退化检测若忽略数据点重新关联，容易受噪声和复杂退化影响。
- LiLi扰动优化解并比较所得优化位姿，用se(3)生成元系统描述退化变换集合。
- 合成数据上相较Hessian方法将配准误差降低50%，真实退化数据中里程计定位更优。
验证范围与局限：验证包括合成数据、真实退化数据中的260 m轨迹和430 m往返隧道轨迹的LiDAR里程计。
英文摘要 (Abstract)
In this paper, we study 3D LiDAR scan alignment in challenging scenarios with degeneracies, such as straight corridors or flat fields, where the alignment solution is not unique and compromises localization and mapping accuracy. Existing degeneracy detection methods that neglect the potential for reassociating data points are prone to being sensitive to noise and complex degeneracies. Therefore, we propose LiLi - a novel method that leverages Lie theory to identify the full set of degenerate transformations within the SE(3) Lie group of rigid transformations. The method employs perturbations of the optimized solution and compares the resulting optimized poses to ensure robust detection of degeneracies. By leveraging generators from the Lie algebra se(3), the method provides a systematic approach to describing the set of degenerate transformations. Quantitative evaluations on synthetic data show significant improvement over the state-of-the-art Hessian-based method, reducing alignment error by 50%, with more significant improvements for datasets featuring noise. In the real-world degenerate datasets, the proposed method integrated into LiDAR-based odometry yields superior localization performance compared to the reference solution based on the Hessian-based degeneracy detector on a 260 m long trajectory, and succeeds on a 430 m long round-trip tunnel trajectory where the reference fails.
作者：Vsevolod Hulchuk, Jan Bayer, Jan Faigl
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