One-shot Global Localization through Semantic Distribution Feature Retrieval and Semantic Topological Histogram Registration

Abstract

One-shot global localization is crucial in many robotic applications, providing significant advantages during initialization and relocalization processes. However, LiDAR-based one-shot global localization methods encounter challenges, including local feature matching errors, sensitivity to dynamic objects, and computational complexity in the absence of an initial pose. To address these issues, we propose a one-shot LiDAR-semantic-graph-based global localization method. To mitigate the interference of dynamic objects on localization, we extract stable semantic objects from LiDAR point clouds using dynamic curved voxel clustering and subsequently construct a semantic graph. Furthermore, we leverage the distribution characteristics of the semantic objects to quickly filter candidate retrievals and construct a cost matrix for the Hungarian algorithm, utilizing a semantic topological histogram to solve vertex matching. This yields a coarse pose estimate, which is subsequently refined using Fast-GICP. We demonstrate the superior localization performance compared to existing state-of-the-art methods on multiple large-scale outdoor datasets, including MulRan, MCD, and Apollo. Our method will be open-sourced and accessible at: https://github.com/Hfx-J/SGGL.