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Communication Dans Un Congrès Année : 2023

MagHT: a Magnetic Hough Transform for fast indoor place recognition

Résumé

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.
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Dates et versions

hal-04178863 , version 1 (08-08-2023)

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Iad Abdul Raouf, Vincent Gay-Bellile, Steve Bourgeois, Cyril Joly, Alexis Paljic. MagHT: a Magnetic Hough Transform for fast indoor place recognition. IROS 2023 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2023, Detroit, United States. pp.3343-3350, ⟨10.1109/IROS55552.2023.10342269⟩. ⟨hal-04178863⟩
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