GraSS: Graph Neural Networks for Loop Closure Detection with Semantic and Spatial Assistance

Loop Closure Detection (LCD) is an essential part of minimizing drift due to the accumulation of previously pose errors in Simultaneous Localization and Mapping (SLAM). The existing loop detection methods are limited by the changes of external conditions such as illumination, viewpoint and appearance. Previous work has mainly focused on the feature descriptor matching methods, which usually only consider the keypoints themselves. Here, we propose a fusion method GraSS, which uses the Graph Neural Network (GNN) based on visual features, and introduces semantics and depth, so as to enhance the spatial characteristics of the keypoints and the information correlation between them in the graph. Furthermore, a learnable parameter is added when two keypoints share the same semantic labels, their matching scores are increased, mitigating to some extent the issue of mismatch caused by significant differences in external conditions between two keypoints that should ideally be paired. Our findings show that GraSS has better performance than other state-of-the-art LCD methods when facing obvious illumination, appearance changes and slight viewpoint changes.