The Harmonic Exponential Filter for Nonparametric Estimation on Motion Groups

TL;DR

This paper presents a novel nonparametric Bayesian filter for motion groups that effectively models multimodal distributions using harmonic exponential distributions, improving state estimation in robotics beyond traditional Gaussian assumptions.

Contribution

Introduces a harmonic exponential filter leveraging Fourier coefficients for efficient nonparametric Bayesian filtering on motion groups, handling multimodal distributions.

Findings

Outperforms existing nonparametric filters in localization tasks

Efficient computation via Fourier domain tensor products

Accurately models multimodal distributions in robotics

Abstract

Bayesian estimation is a vital tool in robotics as it allows systems to update the robot state belief using incomplete information from noisy sensors. To render the state estimation problem tractable, many systems assume that the motion and measurement noise, as well as the state distribution, are unimodal and Gaussian. However, there are numerous scenarios and systems that do not comply with these assumptions. Existing nonparametric filters that are used to model multimodal distributions have drawbacks that limit their ability to represent a diverse set of distributions. This paper introduces a novel approach to nonparametric Bayesian filtering on motion groups, designed to handle multimodal distributions using harmonic exponential distributions. This approach leverages two key insights of harmonic exponential distributions: a) the product of two distributions can be expressed as the element-wise addition of their log-likelihood Fourier coefficients, and b) the convolution of two distributions can be efficiently computed as the tensor product of their Fourier coefficients. These observations enable the development of an efficient and asymptotically exact solution to the Bayes filter up to the band limit of a Fourier transform. We demonstrate our filter's performance compared with established nonparametric filtering methods across simulated and real-world localization tasks.