Analysis of Point Set Registration Algorithms for Industrial Parts

Kedilioglu O, Zhao Y, Landesberger M, Hofmann C, Hofmann M, Franke J, Reitelshöfer S (2025)

Publication Type: Authored book

Publication year: 2025

Publisher: Springer Science+Business Media

ISBN: 9783031740107

DOI: 10.1007/978-3-031-74010-7_18

Abstract

In this paper, we investigate the suitability of existing point set registration algorithms for the task of pose estimation of industrial parts. Numerous algorithms with different characteristics have been developed already. However, no comparative analysis regarding the performance in the context of industrial parts have been conducted yet. We implement a comparative study by applying various point set registration algorithms to an industrial part and varying the point cloud density. The results of our study provide insights into the performance of different algorithms and shed light on the best practices for point set registration in the context of industrial parts.

Authors with CRIS profile

Oguz Kedilioglu Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Yang Zhao Lehrstuhl für Kunststofftechnik (LKT) Christian Hofmann Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Sebastian Reitelshöfer Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)

Involved external institutions

Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Kedilioglu, O., Zhao, Y., Landesberger, M., Hofmann, C., Hofmann, M., Franke, J., & Reitelshöfer, S. (2025). Analysis of Point Set Registration Algorithms for Industrial Parts. Springer Science+Business Media.

MLA:

Kedilioglu, Oguz, et al. Analysis of Point Set Registration Algorithms for Industrial Parts. Springer Science+Business Media, 2025.

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