Abstract
This work describes a velocity field controller for a mobile platform based on the estimation of its position using odometry. It is a known fact that this approach has an unbounded error attached. The approach here proposed uses a position estimation obtained by odometry, which uses the six components of the velocity calculated on the basis of the displacement vectors resulted from optical flow estimation. The control loop proposed takes the position estimation as reference and performs a velocity field control. Results obtained shows that, for closed trajectories, the tracking error is bounded.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Stöfler, N., Burkert, G.T.: Farber Real-Time Obstacle Avoidance Using MPEG-Processor-Based Optic Flow Sensor. In: Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, pp. 161–166 (2000)
Hrabar, S., Sukhatme, G.S., Corke, P., Usher, K., Roberts, J.: Combined Optic-Flow and Stereo-Based Navigation of Urban Canyons for an UAV. In: Proceedings International Conference on Intelligent Robots and Systems - IROS 2005, Edmonton, Canada, pp. 3309–3316 (2005)
Stöfler, N.O., Schnepf, Z.: An MPEG-Processor-based Robot Vision System for Real-Time Detection of Moving Objects by a Moving Observer. In: Proceedings of the 14th International Conference on Pattern Recognition, Brisbane, Australia, pp. 477–481 (1998)
Kelly, R., Bugarín, E., Campa, R.: Application of Velocity Field Control to Visual Navigation of Mobile Robots. In: Proceedings of the 5th IFAC Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal (2004)
Medina-Meléndez, W., Fermín, L., Cappelletto, J., Murrugarra, C., Fernández-López, G., Grieco, J.C.: Vision-Based Dynamic Velocity Field Generation for Mobile Robots. In: Robot Motion Control 2007 - RoMoCo 2007. Lecture Notes in Control and Information Sciences, vol. 360, pp. 69–79 (2007)
Horn, B.K.P., Schunk, B.G.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)
Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings DARPA Imaging Understanding Workshop, pp. 121–130 (1981)
Stöfler, N.O., Farber, G.: An Image Processing Board with an MPEG Processor and Additional Confidence Calculation for Fast and Robust Optic Flow Generation in Real Environments. In: Proceedings of the 8th IEEE International Conference on Advance Robotics, Monterey, USA, pp. 845–850 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fermín, L., Medina-Meléndez, W., Grieco, J.C., Fernández-López, G. (2008). Optical Flow Based Velocity Field Control. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_74
Download citation
DOI: https://doi.org/10.1007/978-3-540-69731-2_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
eBook Packages: Computer ScienceComputer Science (R0)