Filter Early, Match Late: Improving NetworkBased Visual Place Recognition CNNs have excelled at performing place recognition over time, particularly when the neural network is optimized for localization in the current environmental conditions. In this paper we investigate the concept of feature map filtering, where, rather than using all the activations within a convolutional tensor, only the most useful activations are used. Since specific feature maps encode different visual features, the objective is to remove feature maps that detract from the ability to recognize a location across appearance changes. Our key innovation is to filter the feature maps in an early convolutional layer, but then continue to run the network and extract a feature vector using a later, more viewpoint invariant layer in the same network. Our approach improves the condition and viewpoint invariance of a pretrained network, using as little as a single training image pair from the deployment environment. An exhaustive experimental analysis is performed to determine the full scope of causality between early layer filtering and late layer extraction. For validity, we use three datasets: Oxford RobotCar, Nordland, and Gardens Point, achieving overall superior performance to NetVLAD. The work provides a number of new avenues for exploring CNN optimizations, without requiring any retraining of the network weights.

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.