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

2024-12-03

arXiv.org

AI Summary

LiDAR-based localization suffers from poor robustness in degenerate environments (e.g., corridors, tunnels) due to insufficient geometric features. Method: This paper proposes the first sensor-data-driven localizability assessment framework, enabling proactive prediction and avoidance of registration failure. We introduce a novel *predictive alignment risk assessment* mechanism that quantifies the local observability of LiDAR-inertial systems *prior to optimization*, integrating point-cloud geometric structure analysis, motion prior modeling, and risk-aware optimization into an end-to-end risk-perceptive localization system. Contribution/Results: Evaluated across eight challenging degenerate scenarios, our method achieves a 54% improvement in localization accuracy over state-of-the-art approaches while significantly enhancing robustness. To foster reproducibility and further research, we publicly release both the source code and a real-world dataset collected in degenerate environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

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

Problem

• Enhancing LiDAR localization robustness in degraded environments
• Predicting alignment risks to prevent localization failures early
• Improving accuracy in feature-scarce scenarios like tunnels and caves

Innovation

• Predicts alignment risks early
• Evaluates raw sensor localizability
• Improves accuracy by 54%