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Proceedings Paper

A review of the dataset available for visual odometry
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Paper Abstract

During the last two decades the number of visual odometry algorithms has grown rapidly. While it is straightforward to obtain a qualitative result, if the shape of the trajectory is in accordance with the movement of the camera, a quantitative evaluation is needed to evaluate the performances and to compare algorithms. In order to do so, one needs to establish a ground truth either for the overall trajectory or for each camera pose. To this end several datasets have been created. We propose a review of the datasets created over the last decade. We compare them in terms of acquisition settings, environment, type of motion and the ground truth they provide. The purpose is to allow researchers to rapidly identifies the datasets that best fit their work. While the datasets cover a variety of techniques to establish a ground truth, we provide also the reader with techniques to create one that were not present among the reviewed datasets.

Paper Details

Date Published: 16 July 2019
PDF: 8 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720W (16 July 2019); doi: 10.1117/12.2521750
Show Author Affiliations
Martin Rebert, Institut Franco-Allemand de Recherches de Saint-Louis (France)
IRIMAS, Univ. of Haute-Alsace (France)
David Monnin, Institut Franco-Allemand de Recherches de Saint-Louis (France)
Stéphane Bazeille, IRIMAS, Univ. of Haute-Alsace (France)
Christophe Cudel, IRIMAS, Univ. of Haute-Alsace (France)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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