External Vision based Robust Pose Estimation System for a Quadrotor in Outdoor Environments

Wei Zheng, Fan Zhou, Zengfu Wang

2014

Abstract

In this paper, an external vision based robust pose estimation system for a quadrotor in outdoor environments has been proposed. This system could provide us with approximate ground truth of pose estimation for a quadrotor outdoors, while most of external vision based systems perform indoors. Here, we do not modify the architecture of the quadrotor or put colored blobs, LEDs on it. Only using the own features of the quadrotor, we present a novel robust pose estimation algorithm to get the accurate pose of a quadrotor. With good observed results, we get all the four rotors and calculate the pose. But when fewer than four rotors are observed, all of existing external vision based systems of the quadrotor do not mention this and could not get right pose results. In this paper, we have solved this problem and got accurate pose estimation with IMU(inertial measurement unit) data. This system can provide us with approximate ground truth outdoors. We demonstrate in real experiments that the vision-based pose estimation system for outdoor environments can perform accurately and robustly in real time.

References

  1. Abeywardena, D., Wang, Z., Kodagoda, S., and Dissanayake, G. (2013). Visual-inertial fusion for quadrotor micro air vehicles with improved scale observability. In ICRA, pages 3148-3153.
  2. Achtelik, M., Zhang, T., Kuhnlenz, K., and Buss, M. (2009). Viusal tracking and control of a quadcopter using a stereo camera system and inertial sensors. In ICMA, pages 2863-2869.
  3. Ahrens, S., Levine, D., Andrews, G., and How, J. P. (2009). Vision-based guidance and control of a hovering vehicle in unknown, gps-denied environments. In ICRA, pages 2643-2648.
  4. Altug, E., Ostrowski, J. P., and Taylor, C. J. (2003). Quadrotor control using dual camera visual feedback. In ICRA, pages 4294-4299.
  5. Ansar, A. and Daniilidis, K. (2003). Linear pose estimation from points or lines. In PAMI, 25:578-589.
  6. Breitenmoser, A., Kneip, L., and Siegwart, R. (2011). A monocular vision-based system for 6d relative robot localization. In IROS, pages 79-85.
  7. Dementhon, D. F. and Davis, L. S. (1995). Model-based object pose in 25 lines of code. In IJCV, 15:123-141.
  8. Fraundorfer, F., Transkanen, P., and Pollefeys, M. (2010). A minimal case solution to the calibrated relative pose problem for the case of two known orientation angles. In ECCV, pages 269-282.
  9. Gao, X.-S., Hou, X.-R., Tang, J., and Cheng, H.-F. (2003). Complete solution classification for the perspectivethree-point problem. In PAMI, 25:930-943.
  10. Ha, C. and Lee, D. (2013). Vision-based teleoperation of unmanned aerial and ground vehicles. In ICRA, pages 1465-1470.
  11. Hartley, R. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision (Second Edition). Cambridge University Press.
  12. How, J. P., Bethke, B., Frank, A., Dale, D., and Vian, J. (2008). Real-time indoor autonomous vehicle test environment. IEEE Control Systems Magazine, 28:51- 64.
  13. Hu, Z. and Wu, F. (2002). A note on the number of solutions of the noncoplanar p4p problem. In PAMI, 24:550- 555.
  14. Kukelova, Z., Bujnak, M., and Pajdla, T. (2010). Closedform solutions to the minimal absolute pose problems with known vertical direction. In ACCV, pages 216- 229.
  15. Lepetit, V., Moreno-Noguer, F., and Fua, P. (2008). Epnp: Accurate non-iterative o(n) solution to the pnp problem. In IJCV, 81:151-166.
  16. Lim, H., Sinha, S. N., Cohen, M. F., and Uyttendaele, M. (2012). Real-time image-based 6-dof localization in large-scale environments. In CVPR, pages 1043- 1050.
  17. Lu, C., Hager, G., and Mjolsness, E. (2000). Fast and globally convergent pose estimation from video images. In PAMI, 22:610-622.
  18. Quan, L. and Lan, Z. (1999). Linear n-point camera pose determination. In PAMI, 21:774-780.
  19. Schweighofer, G. and Pinz, A. (2006). Robust pose estimation from a planar target. In PAMI, 28:2024-2030.
  20. Wendel, A., Irschara, A., and Bischof, H. (2011). Natural landmark-based monocular localization for mavs. In ICRA, pages 5792-5799.
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Paper Citation


in Harvard Style

Zheng W., Zhou F. and Wang Z. (2014). External Vision based Robust Pose Estimation System for a Quadrotor in Outdoor Environments . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 718-723. DOI: 10.5220/0004906007180723


in Bibtex Style

@conference{icpram14,
author={Wei Zheng and Fan Zhou and Zengfu Wang},
title={External Vision based Robust Pose Estimation System for a Quadrotor in Outdoor Environments},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={718-723},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004906007180723},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - External Vision based Robust Pose Estimation System for a Quadrotor in Outdoor Environments
SN - 978-989-758-018-5
AU - Zheng W.
AU - Zhou F.
AU - Wang Z.
PY - 2014
SP - 718
EP - 723
DO - 10.5220/0004906007180723