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Loop closure is necessary for correcting errors accumulated in simultaneouslocalization and mapping (SLAM) in unknown environments. However, conventionalloop closure methods based on low-level geometric or image features may causehigh ambiguity by not distinguishing similar scenarios. Thus, incorrect loopclosures can occur. Though semantic 2D image information is considered in someliterature to detect loop closures, there is little work that compares 3Dscenes as an integral part of a semantic SLAM system. This paper introduces anapproach, called SmSLAM+LCD, integrated into a semantic SLAM system to combinehigh-level 3D semantic information and low-level feature information to conductaccurate loop closure detection and effective drift reduction. Theeffectiveness of our approach is demonstrated in testing results.

Towards Accurate Loop Closure Detection in Semantic SLAM with 3D Semantic Covisibility Graphs