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Authors: Safa Ouerghi ; Nicolas Ragot ; Remi Boutteau and Xavier Savatier

Affiliation: Normandie Univ., UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France

Keyword(s): Intel T265, ORB-SLAM2, Benchmarking, Person Localization.

Abstract: Aiming at localizing persons in industrial sites is a major concern towards the development of the factory of the future. During the last years, developments have been made in several active research domains targeting the localization problem, among which the vision-based Simultaneous Localization and Mapping paradigm. This has led to the development of multiple algorithms in this field such as ORB-SLAM2 known to be the most complete method as it incorporates the majority of the state-of-the-art techniques. Recently, new commercial and low-cost systems have also emerged in the market that can estimate the 6-DOF motion. In particular, we refer here to the Intel Realsense T265, a standalone 6-DOF tracking sensor that runs a visual-inertial SLAM algorithm and that accurately estimates the 6-DOF motion as claimed by the Intel company. In this paper, we present an evaluation of the Intel T265 tracking camera by comparing its localization performances to the ORB-SLAM2 algorithm. This bench marking fits within a specific use-case: the person localization in an industrial site. The experiments have been conducted in a platform equipped with a VICON motion capture system, which physical structure is similar to a one that we could find in an industrial site. The Vicon system is made of fifteen high-speedtracking cameras (100 Hz) which provides highly accurate poses that were used as ground truth reference. The sequences have been recorded using both an Intel RealSense D435 camera to use its stereo images with ORB-SLAM2 and the Intel RealSense T265. The two sets of timestamped poses (VICON and the ones provided by the cameras) were aligned then calibrated using the point set registration method. The Absolute Trajectory Error, the Relative Trajectory Error and the Euclidian Distance Error metrics were employed to benchmark the localization accuracy from ORB-SLAM2 and T265. The results show a competitive accuracy of both systems for a handheld camera in an indoor industrial environment with a better reliability with the T265 Tracking system. (More)

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Paper citation in several formats:
Ouerghi, S.; Ragot, N.; Boutteau, R. and Savatier, X. (2020). Comparative Study of a Commercial Tracking Camera and ORB-SLAM2 for Person Localization. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 357-364. DOI: 10.5220/0008980703570364

@conference{visapp20,
author={Safa Ouerghi. and Nicolas Ragot. and Remi Boutteau. and Xavier Savatier.},
title={Comparative Study of a Commercial Tracking Camera and ORB-SLAM2 for Person Localization},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={357-364},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008980703570364},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Comparative Study of a Commercial Tracking Camera and ORB-SLAM2 for Person Localization
SN - 978-989-758-402-2
IS - 2184-4321
AU - Ouerghi, S.
AU - Ragot, N.
AU - Boutteau, R.
AU - Savatier, X.
PY - 2020
SP - 357
EP - 364
DO - 10.5220/0008980703570364
PB - SciTePress