  In this paper, we propose a unified information theoretic framework for
learning-motivated methods aimed at odometry estimation, a crucial component of
many robotics and vision tasks such as navigation and virtual reality where
relative camera poses are required in real time. We formulate this problem as
optimizing a variational information bottleneck objective function, which
eliminates pose-irrelevant information from the latent representation. The
proposed framework provides an elegant tool for performance evaluation and
understanding in information-theoretic language. Specifically, we bound the
generalization errors of the deep information bottleneck framework and the
predictability of the latent representation. These provide not only a
performance guarantee but also practical guidance for model design, sample
collection, and sensor selection. Furthermore, the stochastic latent
representation provides a natural uncertainty measure without the needs for
extra structures or computations. Experiments on two well-known odometry
datasets demonstrate the effectiveness of our method.
