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This paper introduces an iterative Lie algebra-based optimization framework for the AX=YB hand-eye calibration problem that avoids explicit uncertainty modeling. The method incorporates an uncertainty metric to evaluate relative uncertainty between data sources, dynamically refining the iterative process and synchronizing updates between calibration parameters while strictly preserving structural constraints. Experiments demonstrate that the proposed approach improves estimation accuracy by at least 67% under high-uncertainty conditions compared to existing methods in synthetic datasets.
Hand-eye calibration gets a 67% accuracy boost in high-uncertainty scenarios thanks to a new optimization framework that cleverly avoids explicit uncertainty modeling.
This article proposes a general optimization framework for solving hand-eye calibration problem. Unlike traditional methods, an iterative algorithm based on Lie algebra that achieves approximately global optimal solutions is developed. During the optimization process, the method strictly preserves the structural constraints of the calibration parameters and enables synchronized updates between calibration parameters. Recognizing that data used in real-word hand-eye calibration often contain uncertainty, especially in over-loading and large workspace industrial robot scenarios, which can significantly degrade accuracy, and accurately modeling such uncertainty is inherently difficult, this article avoids explicit uncertainty modeling. Instead, an uncertainty metric to evaluate the relative uncertainty between data sources is introduced and used to dynamically refine the iterative process. To further enhance convergence efficiency, an effective initial solution generation method that improves overall stability and accuracy is designed. Numerical simulations and real-world experiments validate the effectiveness of the proposed approach, and in synthetic datasets, the proposed approach improves the estimation accuracy by at least 67\% under high-uncertainty conditions compared with the existing methods.
