A vision based system for attitude estimation of UAVs

  • Authors:

  • Saul Thurrowgood;Dean Soccol;Richard J. D. Moore;Daniel Bland;Mandyam V. Srinivasan

  • Affiliations:

  • Queensland Brain Institute and the School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD, Australia and Centre of Excellence in Vision Science, Austr ...;Queensland Brain Institute and the School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD, Australia and Centre of Excellence in Vision Science, Austr ...;Queensland Brain Institute and the School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD, Australia and Centre of Excellence in Vision Science, Austr ...;Queensland Brain Institute and the School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD, Australia and Centre of Excellence in Vision Science, Austr ...;Queensland Brain Institute and the School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD, Australia and Centre of Excellence in Vision Science, Austr ...

  • Venue:
  • IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
  • Year:
  • 2009

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Abstract

This paper describes a technique for estimating the attitude of a UAV by monitoring the visual horizon. An algorithm is developed that makes the best use of color and intensity information in an image to determine the position and orientation of the horizon, and infer the aircraft's attitude. The technique is accurate, reliable, and fully capable of real-time operation. Furthermore, it can be incorporated into any existing vision system, irrespective of the way in which the environment is imaged (e.g. through lenses or mirrors).