Interactive learning of visual topological navigation

We present a topological navigation system that is able to visually recognize the different rooms of an apartment and guide a robot between them. Specifically tailored for small entertainment robots, the system relies on vision only and learns its navigation capabilities incrementally by interacting with a user. This continuous learning strategy makes the system particularly adaptable to environmental lighting and structure modifications. From the computer vision point of view, the system uses a purely appearance-based image representation called bag of visual words, without any metric information. This representation was adapted to the incremental context of robotics and supplemented by active perception to enhance performances. Empirical validation on real robots and on the publicly available INDECS image database are presented.