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arXiv 2408.05613cs.RO
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用于求解无数据对应关系手眼标定的生成对抗网络

Generative Adversarial Networks for Solving Hand-Eye Calibration without Data Correspondence

Hyundai Motor Group(现代汽车集团);Korea Institute of Science and Technology(韩国科学技术研究院)

Ilkwon Hong, Junhyoung Ha

AI总结:

本研究重新发掘GAN框架的能力,将无对应关系的标定问题转化为分布对齐的参数估计问题,契合GAN的训练原理,并将其应用于手眼标定,验证了该方法在复杂标定场景的适用性与优势。

摘要:

在本研究中,我们重新发掘出生成对抗网络(GANs)框架可作为无数据对应关系标定问题的求解器。当数据对应关系不存在或建立得较为松散时,标定问题就转化为一个使两类数据分布对齐的参数估计问题。这一过程在概念上与GAN训练的底层原理完全一致——GAN训练中网络被训练为使生成分布匹配真实数据分布。作为主要应用场景,我们将该思路应用于手眼标定问题,证明了所提方法在复杂标定问题中的适用性与优势。

英文摘要:

In this study, we rediscovered the framework of generative adversarial networks (GANs) as a solver for calibration problems without data correspondence. When data correspondence is not present or loosely established, the calibration problem becomes a parameter estimation problem that aligns the two data distributions. This procedure is conceptually identical to the underlying principle of GAN training in which networks are trained to match the generative distribution to the real data distribution. As a primary application, this idea is applied to the hand-eye calibration problem, demonstrating the proposed method's applicability and benefits in complicated calibration problems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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