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.

翻译：由无信息几何引起的退化已知会恶化基于LiDAR的定位与建图性能。本文提出一种新的概率方法，用于检测并缓解点对面误差最小化中的退化效应。点对面优化问题海森矩阵的噪声由构建该矩阵时所用点云数据与表面法向量的噪声特性决定。我们利用这一特性来量化某个方向发生退化的概率。该退化检测流程被应用于一种新的实时退化感知迭代最近点算法中，用于LiDAR配准，在此算法中我们平滑地衰减退化方向上的更新量。方法的参数根据LiDAR数据手册提供的噪声特性进行选择。我们在四项真实世界实验中验证了该方法的有效性，证明其在检测和缓解退化负面影响方面优于现有先进方法。为促进学术交流，我们在以下地址公开了该方法的代码：github.com/ntnu-arl/drpm。