On Learning Heteroscedastic Noise Models within Differentiable Bayes Filters

Alina Kloss, Jeannette Bohg

Abstract:

In many robotic applications, it is crucial to maintain a belief about the state of
a system, like the location of a robot or the pose of an object.
These state estimates serve as input for planning and decision making and
provide feedback during task execution.
Recursive Bayesian Filtering algorithms address the state estimation problem,
but they require a model of the process dynamics and the sensory observations as well as
noise estimates that quantify the accuracy of these models.
Recently, multiple works have demonstrated that the process and sensor models can be
learned by end-to-end training through differentiable versions of Recursive Filtering methods.
However, even if the predictive models are known, finding suitable noise models
remains challenging. Therefore, many practical applications rely on very simplistic noise
models.
Our hypothesis is that end-to-end training through differentiable Bayesian
Filters enables us to learn more complex heteroscedastic noise models for
the system dynamics. We evaluate learning such models with different types of
filtering algorithms and on two different robotic tasks. Our experiments show that especially
for sampling-based filters like the Particle Filter, learning heteroscedastic noise
models can drastically improve the tracking performance in comparison to using
constant noise models.

Keywords: bayesian filtering, heteroscedastic noise, deep learning

TL;DR: We evaluate learning heteroscedastic noise models within different Differentiable Bayes Filters

Data: [KITTI](https://paperswithcode.com/dataset/kitti)