  Estimating ego-pose from cameras is an important problem in robotics with
applications ranging from mobile robotics to augmented reality. While SOTA
models are becoming increasingly accurate, they can still be unwieldy due to
high computational costs. In this paper, we propose to solve the problem by
using invertible neural networks (INN) to find the mapping between the latent
space of images and poses for a given scene. Our model achieves similar
performance to the SOTA while being faster to train and only requiring offline
rendering of low-resolution synthetic data. By using normalizing flows, the
proposed method also provides uncertainty estimation for the output. We also
demonstrated the efficiency of this method by deploying the model on a mobile
robot.
