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.

翻译：闭环检测作为SLAM中的关键组成部分，在纠正累积误差方面具有重要作用。传统基于外观的方法（如词袋模型）常受限于局部二维特征和训练数据规模，导致在真实场景中通用性和鲁棒性不足，易产生闭环漏检或误检。针对这些问题，本文首先提出一种基于多级验证的物体级数据关联方法，能够将当前帧的二维语义特征与地图中的三维物体地标相关联。其次，利用这些关联关系，我们引入一种基于二次曲面子地图拓扑的语义闭环检测方法，通过物体拓扑图表示场景，并比较拓扑图的差异实现大视场下的精准闭环检测。最后，将这两种方法集成到完整的物体感知SLAM系统中。定性实验与消融研究验证了所提物体级数据关联算法的有效性与鲁棒性。定量实验表明，本语义闭环检测方法在精度、召回率和定位精度指标上均优于现有最先进方法。