Robust and accurate visual feature tracking is essential for good pose estimation in visual odometry. However, in fast-moving scenes, feature point extraction and matching are unstable because of blurred images and large image disparity. In this paper, we propose an unsupervised monocular visual odometry framework based on a fusion of features extracted from two sources, that is, the optical flow network and the traditional point feature extractor. In the training process, point features are generated for scene images and the outliers of matched point pairs are filtered by FlannMatch. Meanwhile, the optical flow network constrained by the principle of forward-backward flow consistency is used to select another group of corresponding point pairs. The Euclidean distance between the matching points found by FlannMatch and the corresponding point pairs by the flow network is added to the loss function of the flow network. Compared with SURF, the trained flow network shows more robust performance in complicated fast-motion scenarios. Furthermore, we propose the AvgFlow estimation module, which selects one group of the matched point pairs generated by the two methods according to the scene motion. The camera pose is then recovered by Perspective-n-Point (PnP) or the epipolar geometry. Experiments conducted on the KITTI Odometry dataset verify the effectiveness of the trajectory estimation of our approach, especially in fast-moving scenarios.
Keywords: SLAM; depth network; feature point matching; flow network; trajectory drift; visual odometry.