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Permanent link (DOI): https://doi.org/10.7939/R3QR4P24K

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Vision-Aided Inertial Navigation System Design for Indoor Quadrotors Open Access

Descriptions

Other title
Subject/Keyword
Sensor Fusion
EKF
Vision-based Navigation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Hou, Lianfeng
Supervisor and department
Lynch, Alan (Electrical and Computer Engineering)
Examining committee member and department
Tavakoli, Mahdi (Electrical and Computer Engineering)
Barczyk, Martin (Mechanical Engineering )
Department
Department of Electrical and Computer Engineering
Specialization
Control Systems
Date accepted
2015-09-22T14:42:15Z
Graduation date
2015-11
Degree
Master of Science
Degree level
Master's
Abstract
The navigation task for unmanned aerial vehicles (UAVs), such as quadrotors, in an indoor environment becomes challenging as the global positioning system (GPS) and the magnetometer may provide inaccurate aiding measurements and the signals may get jammed. The navigation system design in this thesis integrates a visual navigation block with a inertial navigation system block, which adds information about aiding measurements information for indoor navigation design. The direct visual measurements are feature coordinates that are obtained from images taken from an onboard monocular camera with different positions in the 3D world space. The scaled relative pose measurements are generated through vision algorithm implementations presented in this thesis. The vehicle states are estimated using the extended Kalman filter (EKF) with inputs from a gyroscope and accelerometer. The EKF sensor fusion process combines inertial measurements and the visual aid- ing measurement to get an optimal estimation. This thesis provides two design results: one navigation system assumes that the 3D world feature coordinates are known and that the navigation system is map-based for the feature ex- traction. The other navigation system does not require prior knowledge of the feature location and captures the feature based on map-less vision algorithms with geometry constraints.
Language
English
DOI
doi:10.7939/R3QR4P24K
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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