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This article proposes a novel indoor magnetic field-based place recognitionalgorithm that is accurate and fast to compute. For that, we modified thegeneralized ''Hough Transform'' to process magnetic data (MagHT). It takes asinput a sequence of magnetic measures whose relative positions are recovered byan odometry system and recognizes the places in the magnetic map where theywere acquired. It also returns the global transformation from the coordinateframe of the input magnetic data to the magnetic map reference frame.Experimental results on several real datasets in large indoor environmentsdemonstrate that the obtained localization error, recall, and precision aresimilar to or are better than state-of-the-art methods while improving theruntime by several orders of magnitude. Moreover, unlike magnetic sequencematching-based solutions such as DTW, our approach is independent of the pathtaken during the magnetic map creation.

MagHT: a Magnetic Hough Transform for Fast Indoor Place Recognition