Place recognition based on deep feature and adaptive weighting of similarity matrix

Effective features and similarity measures are two key points to achieve good performance in place recognition. In this paper we propose an image similarity measurement method based on deep learning and similarity matrix analyzing, which can be used for place recognition and infrastructure-free navigation. In order to obtain high representative feature, Convolutional Neural Networks (CNNs) are adopted to extract hierarchical information of objects in the image. In the method, the image is divided into patches, then the similarity matrix is constructed according to the patch similarities. The overall image similarity is determined by a proposed adaptive weighting scheme based on analyzing the data difference in the similarity matrix. Experimental results show that the proposed method is more robust than the existing methods, and it can effectively distinguish the different place images with similar-looking and the same place images with local changes. Furthermore, the proposed method has the capability to effectively solve the loop closure detection in Simultaneous Locations and Mapping (SLAM).