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Life-long visual localization is one of the most challenging topics in robotics over the last few years. The difficulty of this task is in the strong appearance changes that a place suffers due to dynamic elements, illumination, weather or seasons. In this paper, we propose a novel method (ABLE-M) to cope with the main problems of carrying out a robust visual topological localization along time. The novelty of our approach resides in the description of sequences of monocular images as binary codes, which are extracted from a global LDB descriptor and efficiently matched using FLANN for fast nearest neighbor search. Besides, an illumination invariant technique is applied. The usage of the proposed binary description and matching method provides a reduction of memory and computational costs, which is necessary for long-term performance. Our proposal is evaluated in different life-long navigation scenarios, where ABLE-M outperforms some of the main state-of-the-art algorithms, such as WI-SURF, BRIEF-Gist, FAB-MAP or SeqSLAM. Tests are presented for four public datasets where a same route is traversed at different times of day or night, along the months or across all four seasons.

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In this paper a new vehicle logo recognition approach is presented using Histograms of Oriented Gradients (HOG) and Support Vector Machines (SVM). The system is specifically devised to work with images supplied by traffic cameras where the logos appear with low resolution. A slidingwindow technique combined with a majority voting scheme are used to provide the estimated car manufacturer. The proposed approach is assessed on a set of 3.579 vehicle images, captured by two different traffic cameras that belong to 27 distinctive vehicle manufacturers. The reported results show an overall recognition rate of 92.59%, which supports the use of the system for real applications.