提出新方法提升机器人手眼标定精度，尤其在高不确定性环境下表现更优。
Optimal Uncertainty-Aware Calibration for the AX=YB Problem
- 基于李代数的迭代算法，保持参数结构约束并同步更新
- 在高不确定性下比现有方法精度提升至少67%
- 无需显式建模不确定性，通过动态评估优化收敛
本文提出一种通用优化框架解决手眼标定问题。与传统方法不同，开发了一种基于李代数的迭代算法，可获得近似全局最优解。优化过程中严格保留标定参数的结构约束，并实现参数的同步更新。考虑到实际应用中手眼标定数据常含不确定性，尤其在重载和大工作空间工业机器人场景下，该不确定性会显著降低精度，而准确建模此类不确定性本身极具挑战，因此本文避免显式不确定性建模。取而代之，引入一种不确定性度量，用于评估数据源间的相对不确定性，并动态优化迭代过程。为进一步提升收敛效率，设计了一种有效初始解生成方法，显著提高整体稳定性和精度。数值仿真与真实实验验证了所提方法的有效性，在合成数据集上，该方法在高不确定性条件下相比现有方法估计精度提升至少67%。
原文摘要 · Abstract (English)
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.
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