Learning Optical Flow with R-CNN for Visual Odometry

Abstract

Addressing on monocular visual odometry problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent space of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF latent space is used to compute the regression of the 6-dimensional pose vector. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our model outperforms most learning-based VO approaches.