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

Fuente: arXiv
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Hauptverfasser: Hong, Ilkwon, Ha, Junhyoung
Format: Preprint
Veröffentlicht: 2024
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author Hong, Ilkwon
Ha, Junhyoung
author_facet Hong, Ilkwon
Ha, Junhyoung
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2408_05613
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Generative Adversarial Networks for Solving Hand-Eye Calibration without Data Correspondence
Hong, Ilkwon
Ha, Junhyoung
Robotics
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.
title Generative Adversarial Networks for Solving Hand-Eye Calibration without Data Correspondence
topic Robotics
url https://arxiv.org/abs/2408.05613