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Park marking-based vehicle self-localization with a fisheye topview system

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Abstract

Accurately self-localizing a vehicle is of high importance as it allows to robustify nearly all modern driver assistance functionality, e.g., lane keeping and coordinated autonomous driving maneuvers. We examine vehicle self-localization relying only on video sensors, in particular, a system of four fisheye cameras providing a view surrounding the car, a setup currently growing popular in upper-class cars. The presented work aims at an autonomous parking scenario. The method is based on park markings as orientation marks since they can be found in nearly every parking deck and require only little additional preparation. Our contribution is twofold: (1) we present a new real-time capable image processing pipeline for topview systems extracting park markings and show how to obtain a reliable and accurate ego pose and ego motion estimation given a coarse pose as starting point. (2) The aptitude of this often neglected sensor array for vehicle self-localization is demonstrated. Experimental evaluation yields a precision of 0.15 \(\pm\) 0.18 m and 2.01\(^{\circ }\; \pm\) 1.91\(^{\circ }\).

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Authors and Affiliations

  1. Institut für Neuroinformatik, Ruhr-Universität Bochum, Universitätsstraße 150, Bochum, Germany

    Sebastian Houben, Marcel Neuhausen & Matthias Michael

  2. GIGATRONIK Ingolstadt, Am Augraben 19, Gaimersheim, Germany

    Robert Kesten

  3. Audi Electronics Venture GmbH, Sachsstraße 20, Gaimersheim, Germany

    Florian Mickler & Florian Schuller

Authors
  1. Sebastian Houben
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  2. Marcel Neuhausen
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  3. Matthias Michael
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  4. Robert Kesten
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  5. Florian Mickler
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  6. Florian Schuller
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Correspondence to Sebastian Houben.

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Houben, S., Neuhausen, M., Michael, M. et al. Park marking-based vehicle self-localization with a fisheye topview system. J Real-Time Image Proc 16, 289–304 (2019). https://doi.org/10.1007/s11554-015-0529-z

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  • DOI: https://doi.org/10.1007/s11554-015-0529-z

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