AX=YB問題に対する不確実性を考慮した最適校正法
Optimal Uncertainty-Aware Calibration for the AX=YB Problem
ハンドアイ校正問題に対し、Lie代数に基づく反復最適化で大域的最適解を近似し、データの不確実性を明示的にモデル化せずに相対不確実性指標で反復を動的調整する手法を提案。高不確実性条件下で精度を大幅に向上させた。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yanjia Chen, Xiangfei Li, Huan Zhao, Yiyuan Hong, Guanxiao Xia, Jiexin Zhang, Han Ding
分類: cs.RO
原文アブストラクト
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.
