A Unified Calibration Framework for Coordinate and Kinematic Parameters in Dual-Arm Robots

TL;DR

This paper introduces a unified Lie-algebraic calibration framework for dual-arm robots that jointly optimizes coordinate and kinematic parameters, reducing errors and improving accuracy in vision-based systems.

Contribution

It proposes a novel, integrated calibration method using a Lie-algebraic formulation that mitigates error propagation and provides a certifiable initialization for stable optimization.

Findings

Achieves higher calibration accuracy than previous methods.

Provides a certifiable initialization method with near-global optimality.

Demonstrates robustness and stability in joint parameter optimization.

Abstract

Precise collaboration in vision-based dual-arm robot systems requires accurate system calibration. Recent dual-robot calibration methods have achieved strong performance by simultaneously solving multiple coordinate transformations. However, these methods either treat kinematic errors as implicit noise or handle them through separated error modeling, resulting in non-negligible accumulated errors. In this paper, we present a novel framework for unified calibration of the coordinate transformations and kinematic parameters in both robot arms. Our key idea is to unify all the tightly coupled parameters within a single Lie-algebraic formulation. To this end, we construct a consolidated error model grounded in the product-of-exponentials formula, which naturally integrates the coordinate and kinematic parameters in twist forms. Our model introduces no artificial error separation and thus greatly mitigates the error propagation. In addition, we derive a closed-form analytical Jacobian from this model using Lie derivatives. By exploring the Jacobian rank property, we analyze the identifiability of all calibration parameters and show that our joint optimization is well-posed under mild conditions. This enables off-the-shelf iterative solvers to stably optimize these parameters on the manifold space. Besides, to ensure robust convergence of our joint optimization, we develop a certifiably correct algorithm for initializing the unknown coordinates. Relying on semidefinite relaxation, our algorithm can yield a reliable estimate whose near-global optimality can be verified a posteriori. Extensive experiments validate the superior accuracy of our approach over previous baselines under identical visual measurements. Meanwhile, our certifiable initialization consistently outperforms several coordinate-only baselines, proving its reliability as a starting point for joint optimization.