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.