用图神经网络提升激光雷达子图重定位的鲁棒性与效率
REGRACE: A Robust and Efficient Graph-based Re-localization Algorithm using Consistency Evaluation
- 基于激光雷达子图构建旋转不变特征，融合邻域上下文
- 采用词袋模型加速匹配，识别远距离回环且精度不降
- 不依赖嵌入距离，改用几何一致性判断回环，适合大场景
回环检测对纠正里程计漂移、构建一致地图至关重要，尤其在大规模导航中。现有基于密集点云的方法因扫描匹配计算开销大而难以扩展；对象中心方法虽更高效，却易受视角变化影响。本文提出REGRACE，利用激光雷达子图解决可扩展性与视角差异问题。为每个标注对象提取旋转不变特征，并通过图神经网络增强邻域上下文信息。采用可扩展的词袋方法，每子图池化一个全局特征用于匹配。不依赖嵌入距离，而是通过几何一致性线索定义回环，从而识别远距离回环。实验表明，REGRACE在性能上接近当前最优基线，速度提升约一倍。代码与模型已公开。
原文摘要 · Abstract (English)
Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation. Current methods using dense point clouds for accurate place recognition do not scale well due to computationally expensive scan-to-scan comparisons. Alternative object-centric approaches are more efficient but often struggle with sensitivity to viewpoint variation. In this work, we introduce REGRACE, a novel approach that addresses these challenges of scalability and perspective difference in re-localization by using LiDAR-based submaps. We introduce rotation-invariant features for each labeled object and enhance them with neighborhood context through a graph neural network. To identify potential revisits, we employ a scalable bag-of-words approach, pooling one learned global feature per submap. Additionally, we define a revisit with geometrical consistency cues rather than embedding distance, allowing us to recognize far-away loop closures. Our evaluations demonstrate that REGRACE achieves similar results compared to state-of-the-art place recognition and registration baselines while being twice as fast. Code and models are publicly available.
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