The development of good experimental methodologies for robotics takes often inspiration from general principles of experimental practice. Repeatability prescribes that experiments should involve several trials in order to guarantee that results are not achieved by chance, but are systematic, and statistically significant trends can be identified. In this paper, we propose an approach to improve the repeatability of experiments performed in robotics. In particular, we focus on the domain of SLAM (Simultaneous Localization And Mapping) and we introduce a system that exploits simulations to generate a large number of test data on which SLAM algorithms are automatically evaluated in order to obtain consistent results, according to the principle of repeatability.
Improving Repeatability of Experiments by Automatic Evaluation of SLAM Algorithms / F. Amigoni, V. Castelli, M. Luperto (PROCEEDINGS OF THE ... IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS). - In: IEEE International Conference on Intelligent Robots and Systems[s.l] : Institute of Electrical and Electronics Engineers (IEEE) Computer Society, 2018. - ISBN 97815386-80940. - pp. 7237-7243 (( convegno IROS IEEE/RSJ International Conference on Intelligent Robots and Systems : 1 October 2018 through 5 October tenutosi a Madrid nel 2018.
Improving Repeatability of Experiments by Automatic Evaluation of SLAM Algorithms
M. Luperto
Ultimo
2018
Abstract
The development of good experimental methodologies for robotics takes often inspiration from general principles of experimental practice. Repeatability prescribes that experiments should involve several trials in order to guarantee that results are not achieved by chance, but are systematic, and statistically significant trends can be identified. In this paper, we propose an approach to improve the repeatability of experiments performed in robotics. In particular, we focus on the domain of SLAM (Simultaneous Localization And Mapping) and we introduce a system that exploits simulations to generate a large number of test data on which SLAM algorithms are automatically evaluated in order to obtain consistent results, according to the principle of repeatability.File | Dimensione | Formato | |
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