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.

通过多级验证的对象级数据关联方法和基于二次曲面对象地图拓扑结构的语义闭环方法，我们将其整合到一个完整的对象感知 SLAM 系统中，并在定性实验和定量实验中验证了其有效性、稳健性以及在精度、召回率和定位精度等度量指标上优于现有方法。