  This article proposes a novel indoor magnetic field-based place recognition
algorithm that is accurate and fast to compute. For that, we modified the
generalized ''Hough Transform'' to process magnetic data (MagHT). It takes as
input a sequence of magnetic measures whose relative positions are recovered by
an odometry system and recognizes the places in the magnetic map where they
were acquired. It also returns the global transformation from the coordinate
frame of the input magnetic data to the magnetic map reference frame.
Experimental results on several real datasets in large indoor environments
demonstrate that the obtained localization error, recall, and precision are
similar to or are better than state-of-the-art methods while improving the
runtime by several orders of magnitude. Moreover, unlike magnetic sequence
matching-based solutions such as DTW, our approach is independent of the path
taken during the magnetic map creation.
