LAUSR

Relocalization is a critical component of robotics applications, it poses challenges due to changes in lighting conditions, weather, and viewing point. Image feature-based approaches are appearance-sensitive, high-level semantic landmark-based methods are ambiguous, and topological map matching-based methods are not robust enough among available solutions. We propose a highly robust and highly expressive semantic descriptor for graph matching. Specifically, we begin by introducing an object-plane co-represented topological graph and a graph propagation algorithm to formulate descriptors for high-level landmarks; we then solve graph matching using the sKM algorithm. Finally, we develop a relocalization system that combines semantic objects and geometric planes for pose optimization and conducts experimental validation on various datasets. Experimental results demonstrate that the suggested method remains effective even when the viewpoint changes by more than 80$^\circ$ on the 2D-3D-S dataset, and the mean orientation error is less than 5$^\circ$ on the sceneNN dataset.