Deep Supervised Hashing with Similar Hierarchy for Place Recognition Place recognition as one of the most significant requirements for longterm simultaneous localization and mapping (SLAM) has been developed rapidly in recent years. Also, deep learning is proved to be more capable than traditional methods to extract features under some complex environments. However, in realworld environments, there are many challenging problems such as viewpoint changes and illumination changes. The existing deep learningbased place recognition in extracting feature phases and matching process is both timeconsuming. Moreover, features extracted from convolution neural network (CNN) are floatingpoint type with high dimension. In this paper, we propose deep supervised hashing for place recognition, where we design a similar hierarchy loss function to learn a model. The model can distinguish the similar images more accurately which is well suitable to place recognition. Besides the model can learn high quality hash codes by maximizing the likelihood of triplet labels. Experiments on several benchmark datasets for place recognition show that our approach is robust to viewpoints, illuminations and season changes with high accuracy. Furthermore, the trained model can extract features and match in real time on CPU with less memory consumption.

Deep Fusion of MultiLayers Salient CNN Features and Similarity Network for Robust Visual Place Recognition * Severe changes in appearance and viewpoint of the same place caused by extreme weather and sharp turning are the major challenges for mobile robotxe2x80x99s autonomous navigation. Coping with these problems, and improving the performance of place recognition is more and more in the focus of robotics research. In this paper, we propose a novel visual place recognition approach with a deep fusion of the salient features and similarity network. The process of recognition can be simplified into two stages: feature representation and similarity function learning, which is trainable endtoend. Instead of extracting features directly from the last convoluted layer of the network, the proposed approach extracts feature of the maxpooling layer of each convolutional module, and then fuses these features together. At this stage, the fully connected layer is not used. In order to measure visual similarity between representations in two images, at the second stage, a novel technology that learns similarity function directly from image representations to measure similarity between two images to rank images according to a similarity score is used. Extensive experiments have been conducted to evaluate the performance by comparison with existing stateoftheart methods, experimental results on five challenging datasets show more accuracy and robustness of the proposed approach under severe changes in appearance and viewpoint.