Semantic Descriptors into Representation for Robust Indoor Visual Place Recognition

Visual place localization (VPL) is a problem finding the closest database image from a query image. Since the outdoor images can be recognized from GPS sensors, VPL in an indoor scene is a difficult problem. Also, Image changes indoors are more severe than outdoors. It is because the position of objects can be easily changed indoors. To tackle this problem, we propose a novel localization dataset with 3D objects considering their physical locations in a scene and encode semantic information using neural networks. Experimental results show that our proposed method outperforms other baseline methods on our localization dataset.