DocumentCode :
495941
Title :
A single-camera feature-based vision system for helicopter autonomous landing
Author :
Cesetti, A. ; Frontoni, E. ; Mancini, A. ; Zingaretti, P. ; Longhi, S.
Author_Institution :
Dipt. di Ing. Inf. Gestionale e dell´´Autom., Univ. Politec. delle Marche, Ancona, Italy
fYear :
2009
fDate :
22-26 June 2009
Firstpage :
1
Lastpage :
6
Abstract :
In this paper a feature based single camera vision system for the safe landing of an unmanned aerial vehicle (UAV) is proposed. The autonomous helicopter used for tests is required to navigate from an initial to a final position in a partially known environment, to locate a landing area and to land on it. The algorithm proposed for the detection of safe landing areas is based on the analysis of optical flow and of mutual geometric position of different kinds of features, observed from different points of view. Vision allows estimating the position and velocity of a set of features with respect to the helicopter while the onboard, hierarchical, behavior-based control system autonomously guides the helicopter. Results, obtained using real data and a real helicopter in a outdoor scenario, show the appropriateness of the vision-based approach. It does not require any artificial landmark (e.g., helipad), is able to estimate correctly and autonomously safe landing areas and is quite robust to occlusions.
Keywords :
computational geometry; computer vision; feature extraction; helicopters; image sequences; remotely operated vehicles; behavior-based control system; helicopter autonomous landing; image sequence; mutual geometric position; optical flow; safe landing area detection; single-camera feature-based vision system; unmanned aerial vehicle; Aircraft navigation; Algorithm design and analysis; Cameras; Geometrical optics; Helicopters; Image motion analysis; Machine vision; Testing; Unmanned aerial vehicles; Velocity control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Robotics, 2009. ICAR 2009. International Conference on
Conference_Location :
Munich
Print_ISBN :
978-1-4244-4855-5
Electronic_ISBN :
978-3-8396-0035-1
Type :
conf
Filename :
5174705
Link To Document :
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