Abstract
Quadcopters are small-sized aerial vehicles with four fixed-pitch propellers. These robots have great potential since they are inexpensive with affordable hardware, and with appropriate software solutions they can accomplish assignments autonomously. They could perform daily tasks in the future, such as package deliveries, inspections, and rescue missions. In this chapter, after an extensive introduction to object recognition and tracking, we present an approach for vision-based autonomous flying of an unmanned quadcopter in various structured environments, such as hallway-like scenes. The desired flight direction is obtained visually, based on perspective clues, in particular the vanishing point. This point is the intersection of parallel lines viewed in perspective, and is sought on the front camera image. For a stable guidance the position of the vanishing point is filtered with different types of probabilistic filters, such as linear Kalman filter, extended Kalman filter, unscented Kalman filter and particle filter. These are compared in terms of the tracking error and also for computational time. A switching control method is implemented. Each of the modes focuses on controlling only one state variable at a time and the objective is to center the vanishing point on the image. The selected filtering and control methods are tested successfully, both in simulation and in real indoor and outdoor environments.
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Notes
- 1.
Amazon testing drones for deliveries, BBC News 2013.
- 2.
German railways to test anti-graffiti drones. BBC News 2013.
- 3.
Video link: https://www.youtube.com/watch?v=S7VQWP7O91k.
References
Abbas S, Zhao S, Mani M, Richard V (2013) AutonomyLab, Ar.Drone drivere in ROS. https://github.com/AutonomyLab/ardrone_autonomy
Ali M (2008) Real time detection of lane markers in urban streets. In: IEEE Intelligent vehicles symposium, Eindhoven, the Netherlands, pp 7–12
Beard C, Chen ZQ, Kumar V, Lee Y, Leon-Salas WD, Rao P (2013) SAVEUS: Saving victims in earthquakes through unified systems. Int J Commun Netw Distrib Sys 10(4):402–420
Benini A, Mancini A, Longhi S (2013) An IMU/UWB/vision-based extended Kalman filter for mini-UAV localization in indoor environment using 802.15. 4a wireless sensor network. J Intell Robot Syst 70(4):461–476
Bills C, Chen J, Saxena A (2011) Autonomous MAV flight in indoor environments using single image perspective clues. In: IEEE international conference on robotics and automation, pp 5776–5783
Bonin-Font F, Ortiz A, Oliver G (2008) Visual navigation for mobile robots: a survey. J Intell Robot Syst 53(3):263–296
Bresciani T (2008) Modeling, identification and control of a quadrotor helicopter. Master’s thesis, Lund University, Sweden
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698
Durrant-Whyte HF (2001) Introduction to estimation and the Kalman Filter. Australian Centre for Field Robotics pp 25–29
Gomez-Balderas JE, Flores G, Carrillo LG, Lozano R (2013) Tracking a ground moving target with a quadrotor using switching control. J Intell Robot Syst 70(1–4):65–78
Gordon N, Slamond D, Smith A (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proc Radar Signal, pp 107–113
Hua MD, Hamel T, Morin P, Samson C (2013) Introduction to feedback control of underactuated VTOL vehicles: a review of basic control design ideas and principles. IEEE Control Syst 33(1):61–75
Kiryati N, Eldar Y, Bruckstein AM (1991) A probabilistic Hough transform. Pattern Recognit 24(4):303–316
Krajník T, Vonásek V, Fišer D, Faigl J (2011) AR-drone as a platform for robotic research and education. Research and education in robotics-EUROBOT. Springer, Berlin, pp 172–186
Lange S, Sünderhauf N, Neubert P, Drews S, Protzel P (2012) Autonomous corridor flight of a UAV using a low-cost and light-weight RGB-D camera. Advances in autonomous mini robots. Springer, Berlin, pp 183–192
Liberzon D (2003) Switching in systems and control. Springer, Berlin
Liou SP, Jain RC (1987) Road following using vanishing points. Comput Vis Graph Image Process 39(1):116–130
Majdik A, Albers-Schoenberg Y, Scaramuzza D (2013) MAV urban localization from Google street view data. In: IEEE international conference on intelligent robots and systems, pp 3979–3986
Odido MD, Madara D (2013) Emerging technologies: use of unmanned aerial systems in the realisation of vision 2030 goals in the counties. Int J Appl Sci Technol 3(8):107–127
Pall E, Mathe K, Tamas L, Busoniu L (2014) Railway track following with the AR. Drone using vanishing point detection. In: IEEE international conference on automation, quality and testing, robotics, pp 1–6
Pavlik JV (2015) Transformation: examining the implications of emerging technology for journalism, media and society. Athens J Mass Media Commun 1(1):9–24
Peterfreund N (1999) Robust tracking of position and velocity with Kalman snakes. IEEE Trans Pattern Anal Mach Intell 21(6):564–569
Quigley M, Conley K, Gerkey B, Faust J, Foote T, Leibs J, Wheeler R, Ng AY (2009) ROS: an open-source Robot Operating System. In: ICRA workshop on open source software, vol 3. p 5
Remondino F, Fraser C (2006) Digital camera calibration methods: considerations and comparisons. Intl Arch Photogramm Remote Sens Spat Inf Sci 36(5):266–272
Ristic B, Arulampalm S, Gordon NJ (2004) Beyond the Kalman filter: particle filters for tracking applications. Artech House Publishers, Boston
Rubinsztejn Y (2011) Automatic detection of objects of interest from rail track images. Master’s thesis, University of Manchester
Schulz D, Burgard W, Fox D, Cremers AB (2001) Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. IEEE International conference on robotics and automation, 2:1665–1670
Shen S, Mulgaonkar Y, Michael N, Kumar V (2013) Vision-based state estimation for autonomous rotorcraft MAVs in complex environments. In: IEEE international conference on robotics and automation, pp 1758–1764
Stephane P, Nicolas B, Pierre E, Frederic DH (2012) AR.Drone Developer Guide
Stephens RS (1991) Probabilistic approach to the Hough transform. Image Vis Comput 9(1):66–71
Tomic T, Schmid K, Lutz P, Domel A, Kassecker M, Mair E, Grixa I, Ruess F, Suppa M, Burschka D (2012) Toward a fully autonomous UAV: research platform for indoor and outdoor urban search and rescue. IEEE Robot Autom Mag 19(3):46–56
Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334
Acknowledgments
This work was supported by the Romanian National Authority for Scientific Research, CNCS-UEFISCDI (Project No. PNII-RU-TE-2012-3-0040) and by grant nr. C.I.2/1.2./2015 of the Technical University of Cluj-Napoca.
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Páll, E., Tamás, L., Buşoniu, L. (2015). Vision-Based Quadcopter Navigation in Structured Environments. In: Busoniu, L., Tamás, L. (eds) Handling Uncertainty and Networked Structure in Robot Control. Studies in Systems, Decision and Control, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-319-26327-4_11
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