How to Choose the Best Embedded Processing Platform for on-Board UAV Image Processing ?
Dries Hulens, Jon Verbeke, Toon Goedemé
2015
Abstract
For a variety of tasks, complex image processing algorithms are a necessity to make UAVs more autonomous. Often, the processing of images of the on-board camera is performed on a ground station, which severely limits the operating range of the UAV. Often, offline processing is used since it is difficult to find a suitable hardware platform to run a specific vision algorithm on-board the UAV. First of all, it is very hard to find a good trade-off between speed, power consumption and weight of a specific hardware platform and secondly, due to the variety of hardware platforms, it is difficult to find a suitable hardware platform and to estimate the speed the user’s algorithm will run on that hardware platform. In this paper we tackle those problems by presenting a framework that automatically determines the most-suited hardware platform for each arbitrary complex vision algorithm. Additionally, our framework estimates the speed, power consumption and flight time of this algorithm for a variety of hardware platforms on a specific UAV.We demonstrate this methodology on two real-life cases and give an overview of the present top processing CPU-based platforms for on-board UAV image processing.
References
- Anthony, D., Elbaum, S., Lorenz, A., and Detweiler, C. (2014). On crop height estimation with UAVs. In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, pages 4805- 4812. IEEE.
- De Wagter, C., Tijmons, S., Remes, B. D., and de Croon, G. C. (2014). Autonomous flight of a 20-gram flapping wing MAV with a 4-gram onboard stereo vision system. In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages 4982-4987. IEEE.
- Ehsan, S. and McDonald-Maier, K. D. (2009). On-board vision processing for small UAVs: Time to rethink strategy. In Adaptive Hardware and Systems, 2009. AHS 2009. NASA/ESA Conference on, pages 75-81. IEEE.
- Ferrick, A., Fish, J., Venator, E., and Lee, G. S. (2012). UAV obstacle avoidance using image processing techniques. In Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on, pages 73-78. IEEE.
- Forster, C., Pizzoli, M., and Scaramuzza, D. (2014). Svo: Fast semi-direct monocular visual odometry. In Proc. IEEE Intl. Conf. on Robotics and Automation.
- Hulens, D. and Vanderstegen, M. (2012). UAV autonoom laten vliegen in een boomgaard. Master's thesis, Dept of Industr.Eng., College University Lessius.
- Kok, J., Gonzalez, L. F., and Kelson, N. (2013). FPGA implementation of an evolutionary algorithm for autonomous unmanned aerial vehicle on-board path planning. Evolutionary Computation, IEEE Transactions on, 17(2):272-281.
- Lin, Y. and Saripalli, S. (2014). Path planning using 3d dubins curve for unmanned aerial vehicles. In Unmanned Aircraft Systems (ICUAS), 2014 International Conference on, pages 296-304. IEEE.
- McGee, T. G., Sengupta, R., and Hedrick, K. (2005). Obstacle detection for small autonomous aircraft using sky segmentation. In Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, pages 4679-4684. IEEE.
- Meier, L., Tanskanen, P., Fraundorfer, F., and Pollefeys, M. (2011). Pixhawk: A system for autonomous flight using onboard computer vision. In Robotics and automation (ICRA), 2011 IEEE international conference on, pages 2992-2997. IEEE.
- Nieuwenhuisen, M. and Behnke, S. (2014). Hierarchical planning with 3d local multiresolution obstacle avoidance for micro aerial vehicles. In Proceedings of the Joint Int. Symposium on Robotics (ISR) and the German Conference on Robotics (ROBOTIK).
- Prouty, R. W. (1995). Helicopter performance, stability, and control.
- Sa, I., Hrabar, S., and Corke, P. (2014). Inspection of polelike structures using a vision-controlled VTOL UAV and shared autonomy. In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, pages 4819-4826. IEEE.
- Shen, S., Mulgaonkar, Y., Michael, N., and Kumar, V. (2013). Vision-based state estimation and trajectory control towards high-speed flight with a quadrotor. In Robotics: Science and Systems. Citeseer.
- Siebert, S. and Teizer, J. (2014). Mobile 3d mapping for surveying earthwork projects using an unmanned aerial vehicle (uav) system. Automation in Construction, 41:1-14.
- Suzuki, T., Amano, Y., and Hashizume, T. (2011). Development of a SIFT based monocular EKF-SLAM algorithm for a small unmanned aerial vehicle. In SICE Annual Conference (SICE), 2011 Proceedings of, pages 1656-1659. IEEE.
- Theys, B., Dimitriadis, G., Andrianne, T., Hendrick, P., and De Schutter, J. (2014). Wind tunnel testing of a VTOL MAV propeller in tilted operating mode. In ICUAS.
- Verbeke, J., Hulens, D., Ramon, H., Goedemé, T., and De Schutter, J. (2014). The design and construction of a high endurance hexacopter suited for narrow corridors.
- Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I-511. IEEE.
- Wenzel, K. E., Masselli, A., and Zell, A. (2011). Automatic take off, tracking and landing of a miniature UAV on a moving carrier vehicle. Journal of intelligent & robotic systems, 61(1-4):221-238.
Paper Citation
in Harvard Style
Hulens D., Verbeke J. and Goedemé T. (2015). How to Choose the Best Embedded Processing Platform for on-Board UAV Image Processing ? . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 377-386. DOI: 10.5220/0005359403770386
in Bibtex Style
@conference{visapp15,
author={Dries Hulens and Jon Verbeke and Toon Goedemé},
title={How to Choose the Best Embedded Processing Platform for on-Board UAV Image Processing ?},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={377-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005359403770386},
isbn={978-989-758-091-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - How to Choose the Best Embedded Processing Platform for on-Board UAV Image Processing ?
SN - 978-989-758-091-8
AU - Hulens D.
AU - Verbeke J.
AU - Goedemé T.
PY - 2015
SP - 377
EP - 386
DO - 10.5220/0005359403770386