  Loop closure, as one of the crucial components in SLAM, plays an essential
role in correcting the 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 positives
detections in loop closure. To address these issues, we first propose a
object-level data association method based on multi-level verification, which
can associate 2D semantic features of current frame with 3D objects landmarks
of map. Next, taking advantage of these association relations, we introduce a
semantic loop closure method based on quadric-level object map topology, which
represents scenes through the topological graph of objects and achieves
accurate loop closure at a wide field of view by comparing differences in the
topological graphs. 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.
