  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.
