3D Object Recognition using Time of Flight Camera with Embedded GPU on Mobile Robots

Benjamin Kelényi, Szilárd Molnár, Levente Tamás

2022

Abstract

The main goal of this work is to analyze the most suitable methods for segmenting and classifying 3D point clouds using embedded GPU for mobile robots. We review the current main approaches including, the point-based, voxel-based and point-voxel-based methods. We evaluated the selected algorithms on different publicly available datasets. Simultaneously, we created a novel architecture based on point-voxel CNN architecture that combines depth imaging with IR. This architecture was designed particularly for pulse-based Time of Flight (ToF) cameras and the primary algorithm’s target being embedded devices. We tested the proposed algorithm on custom indoor/outdoor and public datasets, using different camera vendors.

Download


Paper Citation


in Harvard Style

Kelényi B., Molnár S. and Tamás L. (2022). 3D Object Recognition using Time of Flight Camera with Embedded GPU on Mobile Robots. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-555-5, pages 849-856. DOI: 10.5220/0010972200003124


in Bibtex Style

@conference{visapp22,
author={Benjamin Kelényi and Szilárd Molnár and Levente Tamás},
title={3D Object Recognition using Time of Flight Camera with Embedded GPU on Mobile Robots},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2022},
pages={849-856},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010972200003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - 3D Object Recognition using Time of Flight Camera with Embedded GPU on Mobile Robots
SN - 978-989-758-555-5
AU - Kelényi B.
AU - Molnár S.
AU - Tamás L.
PY - 2022
SP - 849
EP - 856
DO - 10.5220/0010972200003124