通过预测定位风险提升激光雷达惯性定位鲁棒性
SuperLoc: The Key to Robust LiDAR-Inertial Localization Lies in Predicting Alignment Risks
- 提前预测传感器数据的可定位性，而非事后检测失败
- 在隧道等复杂场景下定位精度提升54%
- 适合自动驾驶在恶劣环境下的定位研究
基于地图的激光雷达定位在自动驾驶系统中广泛应用，但在特征缺失的退化环境中面临显著挑战。本文提出SuperLoc，一种增强型激光雷达惯性定位系统，其核心创新在于引入新型预测性对齐风险评估机制，可在优化前早期识别并缓解潜在失败。该方法不依赖后处理分析与启发式阈值，而是直接评估原始传感器测量的可定位性。实验表明，在包括走廊、隧道和洞穴在内的多种退化场景中，SuperLoc性能显著优于现有先进方法，定位精度提升54%，表现出最强鲁棒性。为促进后续研究，我们公开了代码及来自八个挑战性场景的数据集。
原文摘要 · Abstract (English)
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
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