SeGraM: Aligned Coordinate System Aided Semantic Graph Matching Method for Loop Closure Detection

Capturing object semantics and their spatial relationships is crucial to estimating scene similarity for loop closure detection. Existing semantic loop closure detection methods generally treat semantics as landmarks or extract the object topology to compare the similarity of frames. However, they often neglect the absolute spatial distribution of objects, which is essential to capture distinctive features of the scene. A fundamental requirement is to register the spatial coordinates of both frames in a unified reference frame. To address this, we construct aligned coordinate systems between two frames and extract absolute spatial distribution features of objects for loop closure detection. Building on this, we introduce SeGraM, a unified semantic graph matching approach applicable to both indoor and outdoor environments. Specifically, for each pair of semantic graphs, we first establish correspondences between nodes, referred to as node pairs. We then evaluate the geometric and semantic consistency of these pairs, along with the local graph features in the surrounding. To facilitate meaningful comparisons, two node pairs are carefully selected to establish aligned spherical coordinate systems, with ground normals to define the Z axes outdoors. SeGraM is validated in both indoor and outdoor scenarios and is compared with multiple algorithms, demonstrating improvements in loop closure detection accuracy.