Visual Odometry from Omnidirectional Images for Intelligent Transportation

  • Marco MarconEmail author
  • Marco Brando Mario Paracchini
  • Stefano Tubaro
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129)


In this article we use omnidirectional images obtained from equirectangular panoramas of Google MapsTM to estimate camera egomotion. The systems was also tested using a 360 camera. The goal is to provide an effective and accurate positioning system for indoor environments or in urban canyons where GPS signal could be absent. We reformulated classical Computer Vision geometrical constraints for pin-hole cameras, like epipolar and trifocal tensor, to omnidirectional cameras obtaining new and effective equations to accurately reconstruct the camera path using couples or triplets of omnidirectional images. Tests have been performed on straight and curved paths to validate the presented approaches.


Visual odometry Ominidirectional images Trifocal tensor 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marco Marcon
    • 1
    Email author
  • Marco Brando Mario Paracchini
    • 1
  • Stefano Tubaro
    • 1
  1. 1.Dipartimento di ElettronicaInformazione e BioingegneriaMilanItaly

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