  Visual Odometry (VO) estimation is an important source of information for
vehicle state estimation and autonomous driving. Recently, deep learning based
approaches have begun to appear in the literature. However, in the context of
driving, single sensor based approaches are often prone to failure because of
degraded image quality due to environmental factors, camera placement, etc. To
address this issue, we propose a deep sensor fusion framework which estimates
vehicle motion using both pose and uncertainty estimations from multiple
on-board cameras. We extract spatio-temporal feature representations from a set
of consecutive images using a hybrid CNN - RNN model. We then utilise a Mixture
Density Network (MDN) to estimate the 6-DoF pose as a mixture of distributions
and a fusion module to estimate the final pose using MDN outputs from
multi-cameras. We evaluate our approach on the publicly available, large scale
autonomous vehicle dataset, nuScenes. The results show that the proposed fusion
approach surpasses the state-of-the-art, and provides robust estimates and
accurate trajectories compared to individual camera-based estimations.
