Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Information-Theoretic Odometry Learning   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.

14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga