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

Towards View-invariant and Accurate Loop Detection Based on Scene Graph