  Visual Odometry (VO) is used in many applications including robotics and
autonomous systems. However, traditional approaches based on feature matching
are computationally expensive and do not directly address failure cases,
instead relying on heuristic methods to detect failure. In this work, we
propose a deep learning-based VO model to efficiently estimate 6-DoF poses, as
well as a confidence model for these estimates. We utilise a CNN - RNN hybrid
model to learn feature representations from image sequences. We then employ a
Mixture Density Network (MDN) which estimates camera motion as a mixture of
Gaussians, based on the extracted spatio-temporal representations. Our model
uses pose labels as a source of supervision, but derives uncertainties in an
unsupervised manner. We evaluate the proposed model on the KITTI and nuScenes
datasets and report extensive quantitative and qualitative results to analyse
the performance of both pose and uncertainty estimation. Our experiments show
that the proposed model exceeds state-of-the-art performance in addition to
detecting failure cases using the predicted pose uncertainty.
