GeoHash3D: Robust and Efficient 6-DoF Pose Estimation from 3D Marker Sets

Wang L, Mahalinga Upadhyaya K, Kedilioglu O, Hofmann M, Gan W, Hempel N, Mayr P, Reitelshöfer S, Franke J (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Robotics MDPI

Book Volume: 15

Article Number: 157

Journal Issue: 8

DOI: 10.3390/robotics15080157

Abstract

Accurately measuring the six degrees of freedom (6-DoF) pose of a sample is a critical prerequisite for applications in robotic sample handling, optical metrology, and industrial quality control. Our pose estimation problem requires matching a small, partial observation of 4–20 discrete 3D points to a reference point set of up to 50 points. Popular registration algorithms such as Go-ICP, TEASER++, and MAC are designed for large-scale correspondence problems and do not perform reliably in our operating regime. Therefore, we propose an improved geometric hashing method that robustly estimates the rigid transformation in the presence of noise and outliers, and demonstrate its effectiveness and speed using both simulated and real-world datasets.

Authors with CRIS profile

Oguz Kedilioglu Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Sebastian Reitelshöfer Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Helmholtz-Zentrum Hereon DE Germany (DE)

How to cite

APA:

Wang, L., Mahalinga Upadhyaya, K., Kedilioglu, O., Hofmann, M., Gan, W., Hempel, N.,... Franke, J. (2026). GeoHash3D: Robust and Efficient 6-DoF Pose Estimation from 3D Marker Sets. Robotics, 15(8). https://doi.org/10.3390/robotics15080157

MLA:

Wang, Lijiu, et al. "GeoHash3D: Robust and Efficient 6-DoF Pose Estimation from 3D Marker Sets." Robotics 15.8 (2026).

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