  Loop closure is necessary for correcting errors accumulated in simultaneous
localization and mapping (SLAM) in unknown environments. However, conventional
loop closure methods based on low-level geometric or image features may cause
high ambiguity by not distinguishing similar scenarios. Thus, incorrect loop
closures can occur. Though semantic 2D image information is considered in some
literature to detect loop closures, there is little work that compares 3D
scenes as an integral part of a semantic SLAM system. This paper introduces an
approach, called SmSLAM+LCD, integrated into a semantic SLAM system to combine
high-level 3D semantic information and low-level feature information to conduct
accurate loop closure detection and effective drift reduction. The
effectiveness of our approach is demonstrated in testing results.
