NDLI: Complete pose determination for low altitude unmanned aerial vehicle using stereo vision

Content Provider	IEEE Xplore Digital Library
Author	Wang, L.K. Hsieh, S.-C. Hsueh, E.C.-W. Fei-Bin Hsaio Kou-Yuan Huang
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Taiwan (Wang, L.K.; Hsieh, S.-C.; Hsueh, E.C.-W.)
Abstract	A well-developed pose estimation scenario suitable for low altitude unmanned aerial vehicle (UAV) is proposed. By employing dual CCD cameras onboard, the instant pose of UAV can be determined without any use of expensive sensor like gyro. The unscented Kalman filter (UKF) is hereafter introduced to resolve the highly nonlinear system dynamics as well as the measurement process of the pose estimation system. The only measurements recorded are those snapshots of ground targets/landmarks taken by two CCD cameras. The proposed scenario can also detect large angle rotation of UAV. Simulation is conducted via a simple case, both UAV and ground targets are stationary, to show the feasibility and applicability of the proposed scheme. Actual GPS measurement data of ground targets coordinates was recorded for UKF processing. A highlight phenomenon, implied by simulation, reveals that a sudden transition of estimation errors arises at the epoch when the UAV is experiencing a large angle maneuvering up to 180/spl deg/ per second.
Starting Page	108
Ending Page	113
File Size	323794
Page Count	6
File Format	PDF
ISBN	0780389123
DOI	10.1109/IROS.2005.1545000
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Unmanned aerial vehicles Stereo vision Charge coupled devices Charge-coupled image sensors Nonlinear systems Nonlinear dynamical systems Vehicle dynamics Global Positioning System Coordinate measuring machines Estimation error vision unscented Kalman filter attitude
Content Type	Text
Resource Type	Article

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