Information-Theoretic Odometry Learning
- URL: http://arxiv.org/abs/2203.05724v1
- Date: Fri, 11 Mar 2022 02:37:35 GMT
- Title: Information-Theoretic Odometry Learning
- Authors: Sen Zhang, Jing Zhang, Dacheng Tao
- Abstract summary: We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation.
The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
- Score: 83.36195426897768
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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