SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map

Loop closure, as one of the crucial components in SLAM, plays an essential role in correcting accumulated errors. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data, making them less versatile and robust in real-world scenarios, leading to missed detections or false positive detections in loop closure. To address these issues, we first propose a semantic loop detection method based on quadric-level object map topology, which represents scenes through the topological graph of quadric-level objects and achieves accurate loop closure at a wide field of view by comparing differences in the topological graphs. Next, to solve the data association problem between frame and map in loop closure, we propose an object data association method based on multi-level verification, which can associate 2D semantic features of the current frame with 3D object landmarks of the map. Finally, we integrate these two methods into a complete object-aware SLAM system. Qualitative experiments and ablation studies demonstrate the effectiveness and robustness of the proposed object-level data association algorithm. Quantitative experiments show that our semantic loop closure method outperforms existing state-of-the-art methods in terms of precision, recall, and localization accuracy metrics.