SuperLoc: アラインメントリスク予測による堅牢なLiDAR慣性自己位置推定
SuperLoc: The Key to Robust LiDAR-Inertial Localization Lies in Predicting Alignment Risks
最適化前にアラインメントリスクを予測することで、劣化環境でも失敗を早期検知し、LiDAR慣性自己位置推定の精度と堅牢性を大幅に向上させる手法を提案した。
著者: Shibo Zhao, Honghao Zhu, Yuanjun Gao, Beomsoo Kim, Yuheng Qiu, Aaron M. Johnson, Sebastian Scherer
分類: cs.RO
原文アブストラクト
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
