SuperLoc: The Key to Robust LiDAR-Inertial Localization Lies in Predicting Alignment Risks

TL;DR

SuperLoc introduces a predictive alignment risk assessment for LiDAR localization, significantly enhancing robustness and accuracy in degraded environments by evaluating raw sensor data before optimization.

Contribution

It presents a novel predictive alignment risk assessment technique that improves LiDAR localization robustness in challenging scenarios, surpassing existing degeneracy mitigation methods.

Findings

54% increase in localization accuracy

Superior robustness in tunnels, caves, and corridors

Effective pre-optimization failure detection

Abstract

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