Topological Localization using Wi-Fi and Vision merged into FABMAP framework

Abstract : — This paper introduces a topological localization algorithm that uses visual and Wi-Fi data. Its main contribution is a novel way of merging data from these sensors. By making Wi-Fi signature suited to FABMAP algorithm, it develops an early-fusion framework that solves global localization and kidnapped robot problem. The resulting algorithm is tested and compared to FABMAP visual localization, over data acquired by a Pepper robot in an office building. Several constraints were applied during acquisition to make the experiment fitted to real-life scenarios. Without any tuning, early-fusion surpasses the performances of visual localization by a significant margin: 94% of estimated localizations are less than 5m away from ground truth compared to 81% with visual localization.
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Communication dans un congrès
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep 2017, Vancouver, France. IEEE, 2017, 〈10.1109/IROS.2017.8206171〉
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01691920
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Mathieu Nowakowski, Cyril Joly, Sébastien Dalibard, Nicolas Garcia, Fabien Moutarde. Topological Localization using Wi-Fi and Vision merged into FABMAP framework. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep 2017, Vancouver, France. IEEE, 2017, 〈10.1109/IROS.2017.8206171〉. 〈hal-01691920〉

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