Visual topological localization is a process typically required by varied mobile autonomous robots, but it is a complex task if long operating periods are considered. This is because of the appearance variations suffered in a place: dynamic elements, illumination or weather. Due to these problems, long-term visual place recognition across seasons has become a challenge for the robotics community. For this reason, we propose an innovative method for a robust and efficient life-long localization using cameras. In thispaper,wedescribeourapproach(ABLE),whichincludes three different versions depending on the type of images: monocular,stereoandpanoramic.Thisdistinctionmakesour proposal more adaptable and effective, because it allows to exploit the extra information that can be provided by each typeofcamera.Besides,wecontributeanovelmethodology for identifying places, which is based on a fast matching of global binary descriptors extracted from sequences of images. The presented results demonstrate the benefits of using ABLE, which is compared to the most representative state-of-the-art algorithms in long-term conditions.

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• Are you ABLE to perform a life-long visual topological localization?