提出概率方法检测激光雷达点云配准中的退化问题
Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization
- 基于点与法向量噪声建模，量化退化方向概率
- 实测验证优于现有方法，显著提升配准稳定性
- 适合激光雷达定位与建图场景，尤其在弱几何区域
由无信息几何引起的退化会严重损害基于激光雷达的定位与建图性能。本文提出一种新的概率方法，用于检测并缓解点到平面误差最小化中的退化影响。通过分析构建优化问题所用点和表面法向量的噪声，表征点到平面优化问题海森矩阵的噪声特性，并据此量化某方向退化的概率。该退化检测过程被集成于一种新型实时退化感知的迭代最近点算法中，通过在退化方向上平滑衰减更新来改善配准。方法参数依据激光雷达数据手册提供的噪声特性设定。我们在四个真实场景实验中验证了该方法，结果表明其在检测与缓解退化负面影响方面优于当前最优方法。代码已开源：github.com/ntnu-arl/drpm。
原文摘要 · Abstract (English)
Degeneracies arising from uninformative geometry are known to deteriorate LiDAR-based localization and mapping. This work introduces a new probabilistic method to detect and mitigate the effect of degeneracies in point-to-plane error minimization. The noise on the Hessian of the point-to-plane optimization problem is characterized by the noise on points and surface normals used in its construction. We exploit this characterization to quantify the probability of a direction being degenerate. The degeneracy-detection procedure is used in a new real-time degeneracy-aware iterative closest point algorithm for LiDAR registration, in which we smoothly attenuate updates in degenerate directions. The method's parameters are selected based on the noise characteristics provided in the LiDAR's datasheet. We validate the approach in four real-world experiments, demonstrating that it outperforms state-of-the-art methods at detecting and mitigating the adverse effects of degeneracies. For the benefit of the community, we release the code for the method at: github.com/ntnu-arl/drpm.
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