Proprioceptive-only State Estimation for Legged Robots with Set-Coverage Measurements of Learned Dynamics

TL;DR

This paper introduces a set-coverage based noise characterization method for proprioceptive-only state estimation in legged robots, improving robustness and consistency over Gaussian assumptions in real-world noisy conditions.

Contribution

It proposes a novel set-coverage measurement approach that does not assume noise distribution, enhancing state estimation robustness for legged robots.

Findings

The method remains consistent under real noise scenarios.

It outperforms Gaussian-based methods in preventing drift.

Validated on both simulation and real-world datasets.

Abstract

Proprioceptive-only state estimation is attractive for legged robots since it is computationally cheaper and is unaffected by perceptually degraded conditions. The history of joint-level measurements contains rich information that can be used to infer the dynamics of the system and subsequently produce navigational measurements. Recent approaches produce these estimates with learned measurement models and fuse with IMU data, under a Gaussian noise assumption. However, this assumption can easily break down with limited training data and render the estimates inconsistent and potentially divergent. In this work, we propose a proprioceptive-only state estimation framework for legged robots that characterizes the measurement noise using set-coverage statements that do not assume any distribution. We develop a practical and computationally inexpensive method to use these set-coverage measurements with a Gaussian filter in a systematic way. We validate the approach in both simulation and two real-world quadrupedal datasets. Comparison with the Gaussian baselines shows that our proposed method remains consistent and is not prone to drift under real noise scenarios.