Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise

TL;DR

This paper introduces the Max Entropy Moment Kalman Filter (MEM-KF), a novel approach for nonlinear polynomial systems with arbitrary noise, using moment-constrained max-entropy distributions to handle complex beliefs and non-Gaussian noise.

Contribution

The paper presents the MEM-KF, which employs MEDs for belief representation and noise modeling, enabling convex optimization-based filtering without symbolic integration.

Findings

Successfully applied to robotics localization tasks

Handles arbitrary noise distributions effectively

Avoids intractable symbolic integrations

Abstract

Designing optimal Bayes filters for nonlinear non-Gaussian systems is a challenging task. The main difficulties are: 1) representing complex beliefs, 2) handling non-Gaussian noise, and 3) marginalizing past states. To address these challenges, we focus on polynomial systems and propose the Max Entropy Moment Kalman Filter (MEM-KF). To address 1), we represent arbitrary beliefs by a Moment-Constrained Max-Entropy Distribution (MED). The MED can asymptotically approximate almost any distribution given an increasing number of moment constraints. To address 2), we model the noise in the process and observation model as MED. To address 3), we propagate the moments through the process model and recover the distribution as MED, thus avoiding symbolic integration, which is generally intractable. All the steps in MEM-KF, including the extraction of a point estimate, can be solved via convex optimization. We showcase the MEM-KF in challenging robotics tasks, such as localization with unknown data association.