Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-29T11:29:16.949Z Has data issue: false hasContentIssue false
  • English
  • Français

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

Published online by Cambridge University Press:  03 February 2016

G. Campa ,
M. R. Napolitano ,
M. Perhinschi ,
M. L. Fravolini ,
L. Pollini  and
M. Mammarella
G. Campa
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, USA
M. R. Napolitano
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, USA
M. Perhinschi
Affiliation:
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, USA
M. L. Fravolini
Affiliation:
Department of Electronics and Information, Perugia University, Perugia, Italy
L. Pollini
Affiliation:
Department of Electrical Systems and Automation, Pisa University, Pisa, Italy
M. Mammarella
Affiliation:
Department of Electrical Systems and Automation, Pisa University, Pisa, Italy
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft.

Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

Type
Research Article
Information
The Aeronautical Journal , Volume 111 , Issue 1120 , June 2007 , pp. 389 - 396
Copyright
Copyright © Royal Aeronautical Society 2007 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Nalepka, J.P. and Hinchman, J.L., Automated aerial refuelling: extending the effectiveness of unmanned air vehicles, AIAA Modelling and Simulation Technologies Conference and Exhibit, 15-18 August 2005, San Francisco, CA, USA.Google Scholar
2. Zhipu JIN, Z., Shima, T. and Schumacher, C.J., Optimal scheduling for refuelling multiple autonomous aerial vehicles IEEE Transactions on robotics, August 2006, 22, (4).Google Scholar
3. Monda, E.W., Lightsey, E.G. and Key, K., An Investigation of GPS pseudolite based relative navigation, Advances in the Astronautical Sciences, 2004, 3, (529).Google Scholar
4. Valasek, J., Gunnam, K., Kimmett, J., Tandale, M.D., Junkins, J.L. and Hughes, D., Vision-based sensor and navigation system for autonomous air refuelling, J Guidance, Control, and Dynamics, September – October 2005, 28, (5).Google Scholar
5. Fravolini, M.L., Ficola, A., Campa, G., Napolitano M.R. and Seanor, B., Modelling and control issues for autonomous aerial refuelling for UAVs using a probe-drogue refuelling system, J Aerospace Science Technology, 2004, 8, (7), pp 611–618.Google Scholar
6. Pollini, L., Innocenti, M. and Mati, R., Vision algorithms for formation flight and aerial refuelling with optimal marker labeling, AIAA Modelling and Simulation Technologies Conference and Exhibit, 15-18 August 2005, San Francisco, CA, USA.Google Scholar
7. Herrnberger, M., Sachs, G., Holzapfel, F., Tostmann, W. and Weixler, E., Simulation analysis of autonomous aerial refuelling procedures AIAA Guidance, Navigation, and Control Conference and Exhibit, 15-18 August 2005, San Francisco, CA, USA.Google Scholar
8. Kelsey, J.M., Byrne, J., Cosgrove, M., Seereeram, S. and Mehra, R.K., Vision-based relative pose estimation for autonomous rendezvous and docking, 2006 IEEE Aerospace Conference, 4-11 March 2006, Big Sky, MT, USA.Google Scholar
9. Chatterji, G.B., Menon, P.K. and Sridhar, B., GPS/Machine vision navigation system for aircraft, IEEE Transactions on Aerospace and Electronic Systems, July 1997, 33, (3), pp 1012–1025.Google Scholar
10. Ross, S.M., Pachter, M., Jacques, D.R., Kish, B.A. and Millman, D.R., Autonomous aerial refuelling based on the tanker reference frame, 2006 IEEE Aerospace Conference, 4-11 March 2006, Big Sky, MT, USA.Google Scholar
11. Nguyen, B.T. and Lin, L.T., The use of flight simulation and flight testing in the automated aerial refuelling program, AIAA Modelling and Simulation Technologies Conference and Exhibit, 15-18 August 2005, San Francisco, CA, USA.Google Scholar
12. Burns, S.R., Clark, C.S. and Ewart, R., The automated aerial refuelling simulation at the AVTAS Laboratory, AIAA Modelling and Simulation Technologies Conference and Exhibit, 15-18 August 2005, San Francisco, CA, USA.Google Scholar
13. Campa, G., Napolitano, M.R. and Fravolini, M.L., A simulation environment for machine vision based aerial refuelling for UAVs, Submitted to: IEEE Transaction On Aerospace and Electronic Systems, April 2006, accepted with minor revisions November 2006.Google Scholar
14. Stevens, B.L. and Lewis, F.L., Aircraft Control and Simulation, John Wiley & Sons, 1987, New York, USA.Google Scholar
15. Addington, G.A. and Myatt, J.H., Control-surface deflection effects on the innovative control effectors (ICE 101) design, Air Force Report, AFRL-VA-WP-TR-2000-3027, June 2000.Google Scholar
16. ir Force Online Database 2005.: www.af.mil/factsheets.Google Scholar
17. Image Processing Toolbox for use with MATLAB: User’s Guide September 2003, The Mathworks Inc.Google Scholar
18. Hutchinson, S., Hager, G. and Corke, P., A tutorial on visual servo control, IEEE Transactions on Robotics and Automation, 1996, 12, (5), pp 651–670.Google Scholar
19. Umeyama, S., Parameterised point pattern matching and its application to recognition of object families, IEEE Transactions on pattern analysis and machine intelligence, 15, (2), 1993, pp 136–144.Google Scholar
20. Pla, F. and Marchant, J.A., Matching feature points in image sequences through a region-based method, Computer vision and image understanding, 1997, 66, (3), pp 271–285.Google Scholar
21. Fravolini, M.L., Campa, G., Napolitano, M.R. and Ficola, A., Evaluation of machine vision algorithms for autonomous aerial refuelling for unmanned aerial vehicles, Submitted to: AIAA J Aerospace Computing, Information and Communication, April 2005.Google Scholar
22. Campa, G., Mammarella, M., Napolitano, M.R., Fravolini, M.L., Pollini, L. and Stolarik, B., A comparison of pose estimation algorithms for machine vision based aerial refuelling for UAV, Mediterranean Control Conference 2006, 28-30 June 2006, Ancona, Italy.Google Scholar
23. Fravolini, M.L., Brunori, V., Ficola, A., La Cava, M. and Campa, G., Feature matching algorithms for machine vision based autonomous arial refuelling, Mediterranean control conference 2006, 28-30 June 2006, Ancona, Italy.Google Scholar
24. Haralick, R.M., et al, Pose estimation from corresponding point data, IEEE Transactions on systems, man, and cybernetics, 1989, 19, (6), pp 1426–1446.Google Scholar
25. Wilson, W., Visual servo control of robots using Kalman filter estimates of robot pose relative to work-pieces, Visual Servoing, Hashimoto, K. (Ed), World Scientific, 1994, pp 71–104.Google Scholar
26. Aggarwal, J. and Nandhakumar, N., On the computing of motion from sequences of images – a review, Proceeding of the IEEE, 1988, 76, (8), pp 917 – 935.Google Scholar
27. Broida, T. and Chellappa, R., Estimating the kinematics and structure of a rigid object from a sequence of monocular images, IEEE transaction on pattern analysis and machine intelligence, 1991, 13, (6), pp 497–513.Google Scholar
28. Soatto, S. and Perona, P., Reducing structure from motion: a general framework for dynamic vision, Part 1: modelling, IEEE transaction on pattern analysis and machine intelligence, 1998, 20, (9), pp 993–942.Google Scholar
29. Lu, C.P., Hager, G.D. and Mjolsness, E., Fast and globally convergent pose estimation from video images, IEEE Transactions On Pattern Analysis and Machine Intelligence, 2000, 22, (6), pp 610–622.Google Scholar
30. Mammarella, M., Addressing pose estimation issues for application of machine vision to UAV autonomous aerial refuelling, (2005), Master Thesis, DSEA, Pisa etd.adm.unipi.it/theses/available/etd-06182005182620/unrestricted/Tesi1_12.pdf.Google Scholar
31. Dell’ Aquila, R., Campa, G., Napolitano, M. R.and Mammarella, M., Real-time machine-vision-based position sensing system for UAV aerial refuelling, J Real-Time Image Processing, 1, (3), pp 213–224, April 2007.Google Scholar