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Geolocalization using Skylines from Omni-Images

Srikumar Ramalingam 1 Sofien Bouaziz 1, 2 Peter Sturm 3 Matthew Brand 1
3 PERCEPTION [2007-2015] - Interpretation and Modelling of Images and Videos [2007-2015]
Inria Grenoble - Rhône-Alpes, LJK [2007-2015] - Laboratoire Jean Kuntzmann [2007-2015], Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019]
Abstract : We propose a novel method to accurately estimate the global position of a moving car using an omnidirectional camera and untextured 3D city models. The camera is oriented upwards to capture images of the immediate skyline, 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. Our contributions include a sky segmentation algorithm for omni-directional images using graph cuts and a novel approach for matching omni-image skylines to 3D models.
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Submitted on : Tuesday, May 24, 2011 - 12:20:43 PM
Last modification on : Thursday, July 23, 2020 - 10:02:02 AM
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Srikumar Ramalingam, Sofien Bouaziz, Peter Sturm, Matthew Brand. Geolocalization using Skylines from Omni-Images. S3DV 2009 - IEEE Workshop on Search in 3D and Video, Sep 2009, Kyoto, Japan. pp.23-30, ⟨10.1109/ICCVW.2009.5457723⟩. ⟨inria-00434339⟩

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