Loop detection plays a key role in visual Simultaneous Localization and Mapping (SLAM) by correcting the accumulated pose drift. In indoor scenarios, the richly distributed semantic landmarks are view-point invariant and hold strong descriptive power in loop detection. The current semantic-aided loop detection embeds the topology between semantic instances to search a loop. However, current semantic-aided loop detection methods face challenges in dealing with ambiguous semantic instances and drastic viewpoint differences, which are not fully addressed in the literature. This paper introduces a novel loop detection method based on an incrementally created scene graph, targeting the visual SLAM at indoor scenes. It jointly considers the macro-view topology, micro-view topology, and occupancy of semantic instances to find correct correspondences. Experiments using handheld RGB-D sequence show our method is able to accurately detect loops in drastically changed viewpoints. It maintains a high precision in observing objects with similar topology and appearance. Our method also demonstrates that it is robust in changed indoor scenes.

翻译：回环检测通过纠正累积的位姿漂移，在视觉同步定位与地图构建（SLAM）中发挥关键作用。在室内场景中，分布丰富的语义地标具有视点不变性，并在回环检测中具备强大的描述能力。当前的语义辅助回环检测方法通过嵌入语义实例间的拓扑关系来搜索回环。然而，现有语义辅助回环检测方法在处理模糊语义实例和剧烈视点差异时面临挑战，这一问题尚未在文献中得到充分解决。本文提出一种基于增量式场景图的新型回环检测方法，面向室内场景的视觉SLAM。该方法联合考虑语义实例的宏观视图拓扑、微观视图拓扑及占用关系，以寻找正确的对应关系。使用手持RGB-D序列进行的实验表明，我们的方法能够在视点剧烈变化下准确检测回环，并在观测具有相似拓扑与外观的物体时保持高精度。该方法还证明了其在变化的室内场景中具有鲁棒性。