The ICP registration algorithm has been a preferred method for LiDAR-based robot localization for nearly a decade. However, even in modern SLAM solutions, ICP can degrade and become unreliable in geometrically ill-conditioned environments. Current solutions primarily focus on utilizing additional sources of information, such as external odometry, to either replace the degenerate directions of the optimization solution or add additional constraints in a sensor-fusion setup afterward. In response, this work investigates and compares new and existing degeneracy mitigation methods for robust LiDAR-based localization and analyzes the efficacy of these approaches in degenerate environments for the first time in the literature at this scale. Specifically, this work proposes and investigates i) the incorporation of different types of constraints into the ICP algorithm, ii) the effect of using active or passive degeneracy mitigation techniques, and iii) the choice of utilizing global point cloud registration methods on the ill-conditioned ICP problem in LiDAR degenerate environments. The study results are validated through multiple real-world field and simulated experiments. The analysis shows that active optimization degeneracy mitigation is necessary and advantageous in the absence of reliable external estimate assistance for LiDAR-SLAM. Furthermore, introducing degeneracy-aware hard constraints in the optimization before or during the optimization is shown to perform better in the wild than by including the constraints after. Moreover, with heuristic fine-tuned parameters, soft constraints can provide equal or better results in complex ill-conditioned scenarios. The implementations used in the analysis of this work are made publicly available to the community.

翻译：近十年来，ICP配准算法一直是基于激光雷达的机器人定位的首选方法。然而，即使在现代SLAM解决方案中，ICP在几何病态环境中也可能退化并变得不可靠。当前的解决方案主要侧重于利用额外的信息来源，例如外部里程计，以替换优化解的退化方向或在后续的传感器融合设置中添加额外约束。为此，本研究首次在此规模上调查并比较了新的和现有的退化缓解方法，以用于鲁棒的基于激光雷达的定位，并分析了这些方法在退化环境中的有效性。具体而言，本研究提出并调查了：i) 将不同类型的约束纳入ICP算法，ii) 使用主动或被动退化缓解技术的影响，以及iii) 在激光雷达退化环境中对病态ICP问题选择使用全局点云配准方法。研究结果通过多个真实世界的实地和模拟实验进行了验证。分析表明，在缺乏可靠外部估计辅助的情况下，主动优化退化缓解对于激光雷达SLAM是必要且有利的。此外，在优化之前或期间引入退化感知的硬约束，在野外环境中表现优于在优化之后纳入约束。而且，通过启发式微调参数，软约束在复杂的病态场景中能够提供同等或更好的结果。本分析中使用的实现已向社区公开提供。