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

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Abstract

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.

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Notes

  1. OpenCV is available from: http://opencv.org/.

  2. More information, extra material, videos and open code (2016b Arroyo, Alcantarilla, Bergasa, and Romera) about ABLE are available from the website of the project: http://www.robesafe.com/personal/roberto.arroyo/openable.html.

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Acknowledgements

This work has been funded in part from the Spanish MINECO through the SmartElderlyCar project (TRA2015-70501-C2-1-R) and from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de actividades I+D (CAM) and cofunded by EU Structural Funds.

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  1. Department of Electronics, University of Alcalá (UAH), 28871, Alcalá de Henares, Madrid, Spain

    Roberto Arroyo, Luis M. Bergasa & Eduardo Romera

  2. iRobot Corporation, 10 Greycoat Place, Victoria, London, UK

    Pablo F. Alcantarilla

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  1. Roberto Arroyo
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Correspondence to Roberto Arroyo.

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Arroyo, R., Alcantarilla, P.F., Bergasa, L.M. et al. Are you ABLE to perform a life-long visual topological localization?. Auton Robot 42, 665–685 (2018). https://doi.org/10.1007/s10514-017-9664-7

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