Map-based LiDAR localization, while widely used in autonomous systems, faces significant challenges in degraded environments due to lacking distinct geometric features. This paper introduces SuperLoc, a robust LiDAR localization package that addresses key limitations in existing methods. SuperLoc features a novel predictive alignment risk assessment technique, enabling early detection and mitigation of potential failures before optimization. This approach significantly improves performance in challenging scenarios such as corridors, tunnels, and caves. Unlike existing degeneracy mitigation algorithms that rely on post-optimization analysis and heuristic thresholds, SuperLoc evaluates the localizability of raw sensor measurements. Experimental results demonstrate significant performance improvements over state-of-the-art methods across various degraded environments. Our approach achieves a 54% increase in accuracy and exhibits the highest robustness. To facilitate further research, we release our implementation along with datasets from eight challenging scenarios

翻译：基于地图的激光雷达定位虽在自主系统中广泛应用，但由于缺乏显著几何特征，其在退化环境中面临重大挑战。本文提出SuperLoc，一个稳健的激光雷达定位框架，旨在解决现有方法的关键局限。SuperLoc采用一种新颖的预测性对齐风险评估技术，能够在优化前早期检测并缓解潜在的定位失败。该方法在走廊、隧道和洞穴等挑战性场景中显著提升了性能。与现有依赖后优化分析和启发式阈值的退化缓解算法不同，SuperLoc直接评估原始传感器测量的可定位性。实验结果表明，在各种退化环境中，本方法相较于前沿方法均实现了显著的性能提升。我们的方法在精度上提高了54%，并展现出最高的鲁棒性。为促进进一步研究，我们公开了实现代码以及来自八个挑战性场景的数据集。