The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 63–68, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-63-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 63–68, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-63-2016

  15 Jun 2016

15 Jun 2016

ROBUST VISION-BASED POSE ESTIMATION ALGORITHM FOR AN UAV WITH KNOWN GRAVITY VECTOR

V. V. Kniaz V. V. Kniaz
  • State Res. Institute of Aviation Systems (GosNIIAS), 125319, 7, Victorenko str., Moscow, Russia

Keywords: UAV, machine vision, external orientation estimation, motion capture system

Abstract. Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.