Salient FlowNet and Decoupled LSTM Network for Robust Visual Odometry An endtoend CNNLSTM network with salient feature attention and contextguided feature selection mechanism for robust visual odometry (VO) on monocular image sequence is developed in this paper. Deep learningbased visual odometry methods have drawn significant concerns comparing with traditional methods. Existing learningbased VO methods usually ignored the redundant features would increase error accumulation, and their rotational and translational motion parameters coupling would enlarge trajectory drift. A scheme on enhancing the visual salient features and decoupling motion parameters to alleviate these problems is investigated in our approach. The classical FlowNet paralleled with a VGGbased salient feature model to reinforce perceptive fields is designed based on an attention mechanism to extract the prominent geometric features from successive monocular images. Furthermore, to reduce the coupling of different motion patterns, a motion decoupled dual Long ShortTerm Memory (LSTM) scheme based on guided feature selection mechanism is designed to select guided features to separately regress rotational and translational parameters. Experiments on KITTI dataset show competitive performance of the proposed approach compared with stateoftheart deep learning based visual odometry methods.

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.