arXiv Open Access 2024

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

Ilkwon Hong Junhyoung Ha

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.RO

Penulis (2)

Ilkwon Hong

Junhyoung Ha

Format Sitasi

APA MLA BibTeX

Hong, I., Ha, J. (2024). Generative Adversarial Networks for Solving Hand-Eye Calibration without Data Correspondence. https://arxiv.org/abs/2408.05613

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2024
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓