SKYLINE2GPS: Localization in Urban Canyons Using Omni-Skylines

    •  Ramalingam, S., Bouaziz, S., Sturm, P., Brand, M., "SKYLINE2GPS: Localization in Urban Canyons Using Omni-skylines", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2010, pp. 3816-3823.
      BibTeX TR2010-097 PDF
      • @inproceedings{Ramalingam2010oct,
      • author = {Ramalingam, S. and Bouaziz, S. and Sturm, P. and Brand, M.},
      • title = {SKYLINE2GPS: Localization in Urban Canyons Using Omni-skylines},
      • booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      • year = 2010,
      • pages = {3816--3823},
      • month = oct,
      • issn = {2153-0858},
      • url = {}
      • }
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  • Research Areas:

    Computer Vision, Robotics


This paper investigates the problem of geolocalization in GPS challenged urban canyons using only skylines. Our proposed solution takes a sequence of upward facing omnidirectional images and coarse 3D models of cities to compute the geo-trajectory. The camera is oriented upwards to capture images of the immediate skylines, which is generally unique and serves as a fingerprint for a specific location in a city. Our goal is to estimate global position by matching skylines extracted from omni-directional images to skyline segments from coarse 3D city models. Under day-time and clear sky conditions, we propose a sky-segmentation algorithm using graph cuts for estimating the geo-location. In cases where the skyline gets affected by partial fog, night-time and occlusions from trees, we propose a shortest path algorithm that computes the location without prior sky detection. We show compelling experimental results for hundreds of images taken in New York, Boston and Tokyo under various weather and lighting conditions (daytime, foggy dawn and night-time).


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