Resampling-free Particle Filters in High-dimensions

TL;DR

This paper presents a novel resampling-free particle filter that effectively addresses particle deprivation in high-dimensional state spaces, improving accuracy and diversity in state estimation for robotics.

Contribution

It introduces a resampling-free particle filter specifically designed for high-dimensional spaces, with theoretical guarantees and empirical validation.

Findings

The proposed filter maintains diverse particles in high dimensions.

It achieves near-accurate posterior representation in synthetic tests.

Effective in 6D pose estimation from videos.

Abstract

State estimation is crucial for the performance and safety of numerous robotic applications. Among the suite of estimation techniques, particle filters have been identified as a powerful solution due to their non-parametric nature. Yet, in high-dimensional state spaces, these filters face challenges such as 'particle deprivation' which hinders accurate representation of the true posterior distribution. This paper introduces a novel resampling-free particle filter designed to mitigate particle deprivation by forgoing the traditional resampling step. This ensures a broader and more diverse particle set, especially vital in high-dimensional scenarios. Theoretically, our proposed filter is shown to offer a near-accurate representation of the desired posterior distribution in high-dimensional contexts. Empirically, the effectiveness of our approach is underscored through a high-dimensional synthetic state estimation task and a 6D pose estimation derived from videos. We posit that as robotic systems evolve with greater degrees of freedom, particle filters tailored for high-dimensional state spaces will be indispensable.