Multi-sensor data fusion for UAV navigation during landing operations

Yang, Xilin, Mejias, Luis, & Garratt, Matt (2011) Multi-sensor data fusion for UAV navigation during landing operations. In Proceedings of the 2011 Australian Conference on Robotics and Automation, Australian Robotics and Automation Association Inc.,Monash University , Monash University, Melbourne, VIC, pp. 1-10.

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This paper presents a practical framework to synthesize multi-sensor navigation information for localization of a rotary-wing unmanned aerial vehicle (RUAV) and estimation of unknown ship positions when the RUAV approaches the landing deck. The estimation performance of the visual tracking sensor can also be improved through integrated navigation. Three different sensors (inertial navigation, Global Positioning System, and visual tracking sensor) are utilized complementarily to perform the navigation tasks for the purpose of an automatic landing. An extended Kalman filter (EKF) is developed to fuse data from various navigation sensors to provide the reliable navigation information. The performance of the fusion algorithm has been evaluated using real ship motion data. Simulation results suggest that the proposed method can be used to construct a practical navigation system for a UAV-ship landing system.

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4 citations in Scopus
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ID Code: 47449
Item Type: Conference Paper
Refereed: Yes
Keywords: Unmanned Aerial Vehicles, Multi-sensor data fusion, Autonomous Landing
ISBN: 9780980740424
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2011 The authors.
Deposited On: 04 Dec 2011 23:21
Last Modified: 27 Dec 2013 04:15

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