Are you ABLE to perform a life-long visual topological localization?
AuthorsArroyo Contera, Roberto; Fernández Alcantarilla, Pablo; Bergasa Pascual, Luis Miguel; Romera Carmena, Eduardo
IdentifiersPermanent link (URI): http://hdl.handle.net/10017/43268
Ministerio de Economía y Competitividad
Comunidad de Madrid
Arroyo, R., Alcantarilla, P.F., Bergasa, L.M. & Romera, E. 2018, “Are you ABLE to perform a life-long visual topological localization?”, Autonomous Robots, vol. 42, no. 3, pp. 665-685
Localization across seasons
Visual place recognition
Loop closure detection
info:eu-repo/grantAgreement/MINECO//TRA2015-70501-C2-1-R/ES/VEHICULO INTELIGENTE PARA PERSONAS MAYORES/
info:eu-repo/grantAgreement/CAM//S2013%2FMIT-2748/ES/ROBOTICA APLICADA A LA MEJORA DE LA CALIDAD DE VIDA DE LOS CIUDADANOS, FASE III/RoboCity2030-III-CM
© 2018 Springer Nature
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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 this paper, we describe our approach (ABLE), which includes three different versions depending on the type of images: monocular, stereo and panoramic. This distinction makes our proposal more adaptable and effective, because it allows to exploit the extra information that can be provided by each type of camera. Besides, we contribute a novel methodology 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.