Multi-Contact Inertial Parameters Estimation and Localization in Legged Robots

TL;DR

This paper presents an advanced optimization framework for accurately estimating inertial parameters and localizing legged robots with multiple contacts, improving over traditional methods through novel solver techniques and physical consistency enforcement.

Contribution

It introduces a multiple shooting solver with Riccati recursion, an inertial manifold for physical consistency, a nullspace approach for singularities, and analytical contact dynamics derivatives, advancing inertial estimation in robotics.

Findings

Successfully estimates inertial parameters during complex maneuvers like brachiation.

Achieves higher accuracy than conventional least squares methods.

Demonstrates effectiveness on the Go1 robot in various tasks.

Abstract

Optimal estimation is a promising tool for estimation of payloads' inertial parameters and localization of robots in the presence of multiple contacts. To harness its advantages in robotics, it is crucial to solve these large and challenging optimization problems efficiently. To tackle this, we (i) develop a multiple shooting solver that exploits both temporal and parametric structures through a parametrized Riccati recursion. Additionally, we (ii) propose an inertial manifold that ensures the full physical consistency of inertial parameters and enhances convergence. To handle its manifold singularities, we (iii) introduce a nullspace approach in our optimal estimation solver. Finally, we (iv) develop the analytical derivatives of contact dynamics for both inertial parametrizations. Our framework can successfully solve estimation problems for complex maneuvers such as brachiation in humanoids, achieving higher accuracy than conventional least squares approaches. We demonstrate its numerical capabilities across various robotics tasks and its benefits in experimental trials with the Go1 robot.