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 49.7% increase in accuracy and exhibits the highest robustness. To facilitate further research, we will release our implementation along with datasets from eight challenging scenarios.

SuperLoc is to our knowledge the first open-source LiDAR-inertial localization system that not only predicts alignment risks, and estimates the observability of scan, but also can actively incorporate pose priors from other odometry sources before failure occurs. The entire process doesn't require predefined heuristic thresholds to detect degeneration and it has been evaluated in various challenging environments including caves, long corridors, flat open areas, and staircases.