A new visual odometry algorithm based on multi-path deep fully convolutional neural networks

Visual odometry is one of the key core technologies in the field of autonomous driving. However, images captured in lowlight or unevenly-illuminated scenes still cannot guarantee good performance due to low image contrast and lack of detail features. Therefore, we propose an end-to-end visual odometry method based on image fusion and FCNN-LSTM in the paper. The brightness image of the source image sequence is obtained by gray-scale transformation, and an image fusion algorithm based on spectral residual theory is designed to combine the image sequence and its brightness image to enhance the contrast of the image and provide more detailed information. In order to improve the accuracy of image feature extraction and reduce the error in the pose estimation process, we design a feature extraction algorithm based on skipfusion-FCNN. The traditional fully convolutional neural network (FCNN) is improved, a skip-fusion-FCNN network model is proposed, and three different paths are constructed for feature extraction. In each path, the prediction results of different depths are fused by downsampling to obtain a feature map. Merge three different feature maps to obtain feature fusion information, taking into account the structural information and detail information of the image. Experiments show that this algorithm is superior to the state-of-the-art algorithms.