Top > JRM > Velocity Estimation for UAVs by Using High-Speed Vision

single-rb.php

JRM Vol.30 No.3 pp. 363-372
doi: 10.20965/jrm.2018.p0363
(2018)

Paper:

Views over last 60 days: 1,683

Velocity Estimation for UAVs by Using High-Speed Vision

Hsiu-Min Chuang*, Tytus Wojtara**, Niklas Bergström**, and Akio Namiki*

*Chiba University
1-33 Yayoi-cho, Inage-ku, Chiba-shi, Chiba 263-8522, Japan

**Autonomous Control Systems Laboratory
Marive West 32F, 2-6-1 Nakase, Mihama-ku, Chiba 261-7132, Japan

Received:
January 18, 2018
Accepted:
May 2, 2018
Published:
June 20, 2018
Creative Commons License
Keywords:
high-speed visual unit, UAV, velocity estimation
Abstract

In recent years, applications of high-speed visual systems have been well developed because of their high environmental recognition ability. These system help to improve the maneuverability of unmanned aerial vehicles (UAVs). Thus, we herein propose a high-speed visual unit for UAVs. The unit is lightweight and compact, consisting of a 500 Hz high-speed camera and a graphic processing unit. We also propose an improved UAV velocity estimation algorithm using optical flows and a continuous homography constraint. By using the high-frequency sampling rate of the high-speed vision unit, the estimation accuracy is improved. The operation of our high-speed visual unit is verified in the experiments.

Linear interpolated optical flow (LIOF)

Linear interpolated optical flow (LIOF)

Cite this article as:
H. Chuang, T. Wojtara, N. Bergström, and A. Namiki, “Velocity Estimation for UAVs by Using High-Speed Vision,” J. Robot. Mechatron., Vol.30 No.3, pp. 363-372, 2018.
Data files:
References
  1. [1] H. Chen, X.-M. Wang, and Y. Li, “A survey of autonomous control for UAV,” IEEE Int. Conf. on Artificial Intelligence and Computational Intelligence, Vol.2, pp. 267-271, 2009.
  2. [2] S. Gupte, P. I. T. Mohandas, and J. M. Conrad, “A survey of quadrotor unmanned aerial vehicles,” Proc. of the IEEE Southeastcon, pp. 1-6, Orlando, Fla, USA, Mar. 2012.
  3. [3] F. Kendoul, Z. Yu, and K. Nonami, “Guidance and nonlinear control system for autonomous flight of minirotorcraft unmanned aerial vehicles,” J. of Field Robotics, Vol.27, Issue 3, pp. 311-334, 2010.
  4. [4] D. Iwakura, W. Wang, K. Nonami, and M. Haley, “Movable Range-Finding Sensor System and Precise Automated Landing of Quad-Rotor MAV” J. of System Design and Dynamics, Vol.5, Issue 1, pp. 17-29, 2011.
  5. [5] K. Nonami, F. Kendoul, S. Suzuki, W. Wang, and D. Nakazawa, “Autonomous Indoor Flight and Precise Automated-Landing Using Infrared and Ultrasonic Sensors,” Autonomous Flying Robots, pp. 303-322, Springer, Japan.
  6. [6] L. Muratet, S. Doncieux, Y. Briere, and J. A. Meyer, “A contribution to vision-based autonomous helicopter flight in urban environments,” Robotics and Autonomous Systems, Vol.50, Issue 4, pp. 195-209, 2005.
  7. [7] K. Mohta, V. Kumar, and K. Daniilidis, “Vision-based control of a quadrotor for perching on lines,” IEEE Int. Conf. on Robot. and Automat., pp. 3130-3136, 2014.
  8. [8] F. Kendoul, I. Fantoni, and K. Nonami, “Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles,” Robotics and Autonomous Systems, Vol.57, Issue 6-7, pp. 591-602, 2009.
  9. [9] B. Herisse, T. Hamel, R. Mahony, and F.-X. Russotto, “Landing a VTOL unmanned aerial vehicle on a moving platform using optical flow,” IEEE Trans. on Robotics, Vol.28, Issue 1, pp. 77-89, 2012.
  10. [10] H. W. Ho, G. C. H. E. de Croon, E. van Kampen, Q. P. Chu, and M. Mulder, “Adaptive Gain Control Strategy for Constant Optical Flow Divergence Landing,” IEEE Trans. on Robotics, Vol.34, Issue 2, April 2018.
  11. [11] S. Weiss, R. Brockers, and L. Matthies, “4dof drift free navigation using inertial cues and optical flow,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 4180-4186, 2013.
  12. [12] K. McGuire, G. de Croon, C. de Wagter, B. Remes, K. Tuyls, and H. Kappen, “Local histogram matching for efficient optical flow computation applied to velocity estimation on pocket drones,” IEEE Int. Conf. on Robot. and Automat., pp. 3255-3260, 2016.
  13. [13] K. McGuire, G. de Croon, C. de Wagter, K. Tuyls, and H. Kappen, “Efficient Optical flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone,” IEEE Robotics and Automation Letters, Vol.2, Issue 2, pp. 1070-1076, 2017.
  14. [14] V. Grabe, H. H. Buelthoff, and P. R. Giordano, “Robust optical-flow based self-motion estimation for a quadrotor UAV,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2153-2159, 2012.
  15. [15] V. Grabe, H. H. Buelthoff, and P. R. Giordano, “On-board velocity estimation and closed-loop control of a quadrotor UAV based on optical flow,” IEEE Int. Conf. on Robot. and Automat., pp. 491-497, 2012.
  16. [16] V. Grabe, H. H. Buelthoff, D. Scaramuzza, and P. R. Giordano, “Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV,” The Int. J. of Robotics Research, Vol.34, Issue 8, pp. 1114-1135, 2015.
  17. [17] M. Ishikawa, A. Morita, and N. Takayanagi, “High speed vision system using massively parallel processing,” Proc. of the lEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Vol.1, 1992.
  18. [18] Y. Nakabo, M. Ishikawa, H. Toyoda, and S. Mizuno, “1 ms column parallel vision system and its application of high speed target tracking,” Proc. IEEE Int. Conf. Robot. and Automat., pp. 650-655, 2000.
  19. [19] M. Ishikawa, A. Namiki, T. Senoo, and Y. Yamakawa, “Ultra high-speed Robot Based on 1 kHz vision system,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 5460-5461, 2012.
  20. [20] T. Senoo, A. Namiki, and M. Ishikawa, “Ball Control in High-Speed Batting Motion using Hybrid Trajectory Generator,” IEEE Int. Conf. on Robot. and Automat., pp. 1762-1767, 2006.
  21. [21] T. Senoo, Y. Horiuchi, Y. Nakanishi, K. Murakami, and M, Ishikawa, “Robotic Pitching by Rolling Ball on Fingers for a Randomly Located Target,” IEEE Int. Conf. on Robot. and Biomimetics, pp. 325-330, 2016.
  22. [22] T. Kizaki and A. Namiki, “Two ball juggling with high-speed hand-arm and high-speed vision system,” IEEE Int. Conf. on Robot. and Automat., pp. 1372-1377, 2012.
  23. [23] A. Namiki, S. Matsushita, T. Ozeki, and K. Nonami, “Hierarchical processing architecture for an air-hockey robot system,” IEEE Int. Conf. on Robot. and Automat., pp. 1187-1192, 2013.
  24. [24] A. Namiki and F. Takahashi, “Motion Generation for a Sword-Fighting Robot Based on Quick Detection of Opposite Player’s Initial Motions,” J. Robot. Mechatron., Vol.27, No.5, pp. 543-551, 2015.
  25. [25] CUDA Accelerated Computer Vision Library, NVIDIA, May 2016.
  26. [26] Visionworks Programming Tutorial, NVIDIA, April 2016.
  27. [27] D. B. Goldman and J.-H. Chen, “Vignette and exposure calibration and compensation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.32, Issue 12, pp. 2276-2288, 2010.
  28. [28] C. Harris and M. Stephens, “A combined corner and edge detector,” Proc. of 4th Alvey vision conf., Vol.15, No.50, pp. 10-5244, 1988.
  29. [29] J. Shi, “Good features to track,” Proc. CVPR’94., IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 593-600, 1994.
  30. [30] Y. Ma, S. Soatto, J. Kosecka, and S. S. Sastry, “An Invitation to 3-D Vision,” Springer, 2004.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 19, 2025

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy