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

Mathieu Nowakowski (1, 2) , Cyril Joly (1) , Sébastien Dalibard (2) , Nicolas Garcia (2) , Fabien Moutarde (1)

1 CAOR - Centre de Robotique
2 SoftBank Robotics Europe

— 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.

Mots clés: low- cost sensors; data fusion; kidnapped robot; topological localization

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

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. ⟨10.1109/IROS.2017.8206171⟩. ⟨hal-01691920⟩