Abstract
Autonomous navigation of quadcopters in unstructured indoor environments is a major problem due to the difficulty of reliable position sensing. While outdoor applications can use GPS for reliable localization, working indoors will require the use of either laser range finders or some other sensors. If the indoor scene is unknown to a robot, the task of mapping new areas also becomes a necessity. The two processes are combined and run together in a framework of Simultaneous Localization and Mapping (SLAM). Our work is focused on using onboard cameras for the task of SLAM in an indoor scenario. Vision based techniques that do not use time of flight methods like laser range finders, have the potential to provide a low cost alternative framework for navigation. In this work, localization using a monocular SLAM framework on an unknown and unstructured scene, a cascaded position controller along with a Luenberger observer which can combine the data of Inertial sensors and vision based position to generate a complete velocity feedback for the system have been used. Sensor data fusion using EKF (Extended Kalman Filter) have been performed for scale estimation. The localization algorithm has been implemented on a quadcopter. Finally hovering experiment has been performed in an indoor lab based environment.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Mellinger, D., Shomin, M., Kumar, V.: Control of quadrotors for robust perching and landing. In: International Powered Lift Conference, pp. 205–225 (2010)
Shen, S., Mulgaonkar, Y., Michael, N., Kumar, V.: Multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft MAV. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4974–4981 (2014)
Hamel, T., Mahony, R.: Visual servoing of an under-actuated dynamic rigid-body system: an image-based approach. IEEE Trans. Robot. Autom. 18(2), 187–198 (2002)
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007)
Engel, J., Sturm, R., Cremers, D.: Accurate figure flying with a quadrocopter using onboard visual and inertial sensing. IMU 320, 240 (2012)
Schauwecker, K., Ke, N.R., Scherer, S.A., Zell, A.: Markerless visual control of a quad-rotor micro aerial vehicle by means of on-board stereo processing. In: Levi, P., Zweigle, O., Häußermann, K., Eckstein, B. (eds.) Autonomous Mobile Systems 2012. Informatik aktuell, pp. 11–20. Springer, Berlin (2012)
Fraundorfer, F., Heng, L., Honegger, D., Lee, G.H., Meier, L., Tanskanen, P., Pollefeys, M.: Vision-based autonomous mapping and exploration using a quadrotor mav. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4557–4564 (2012)
Yang, S., Scherer, S., Zell, A., et al.: Visual SLAM for autonomous MAVs with dual cameras. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5227–5232 (2014)
Achtelik, M., Achtelik, M., Weiss, S., Siegwart, R.: Onboard IMU and monocular vision based control for MAVs in unknown in-and outdoor environments. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3056–3063 (2011)
Nützi, G., Weiss, S., Scaramuzza, D., Siegwart, R.: Fusion of IMU and vision for absolute scale estimation in monocular SLAM. J. Intell. Robot. Syst. 61(1–4), 287–299 (2011)
Weiss, S., Achtelik, M.W., Lynen, S., Chli, M., Siegwart, R.: Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments. In: 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 957–964 (2012)
Weiss, S., Siegwart, R.: Real-time metric state estimation for modular vision-inertial systems. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4531–4537 (2011)
Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semi-direct monocular visual odometry. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22 (2014)
Woodman, O.J.: An introduction to inertial navigation. University of Cambridge, Computer Laboratory, Technical report. UCAMCL-TR-696, vol. 14, p. 15 (2007)
Shree, A.S.: Vision based navigation of a quadcopter using a single camera. Master’s thesis, Indian Institute of Technology, Kanpur, Kanpur, India (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shree, A.S., Sharma, R.S., Behera, L., Venkatesh, K.S. (2017). Position Based Visual Control of the Hovering Quadcopter. In: Basu, A., Das, S., Horain, P., Bhattacharya, S. (eds) Intelligent Human Computer Interaction. IHCI 2016. Lecture Notes in Computer Science(), vol 10127. Springer, Cham. https://doi.org/10.1007/978-3-319-52503-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-52503-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52502-0
Online ISBN: 978-3-319-52503-7
eBook Packages: Computer ScienceComputer Science (R0)