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A comparative study on tightly coupled visual aided inertial navigation systems for unmanned aerial vehicles
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index.pdf
Date
2018
Author
İnce, Talha
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An Inertial Navigation System (INS) is a combination of hardware (accelerometers and gyroscopes) and algorithms to calculate the position, orientation and velocity of a mobile platform. Because of the need to integrate the measurements over time, INS is subjected to cumulative error characteristics, hence cannot provide an accurate navigation solution over long durations. Global Positioning System (GPS) is often used for long time-long distance problems aiding INS. GPS relies on external signals received from satellite networks which gives bounded error. Unfortunately, these signals can be jammed, spoofed or may not be available at indoor applications. Hence, alternatives to the GPS sensor are required. Such a recent alternative is the camera sensor, resulting in Visual Aided Inertial Navigation System (VINS). VINS aims to provide accurate navigation solution using a fusion of imaging and inertial sensor data. The main goal of this thesis is to analyze and increase the performance of VINS sensor fusion algorithms on an unmanned aerial vehicle (UAV). For this purpose, a realistic simulation environment is implemented and different VINS methods are comparatively studied. Extensive simulation studies are conducted to characterize the performance of map-based and mapless VINS methods and to study the effects of performance critical parameters. Finally, a modified Gaussian Mixture Filter and feature region selection method are proposed for increasing the performance of VINS.
Subject Keywords
Inertial navigation systems.
,
Kalman filtering.
,
Global Positioning System.
,
Visual aids.
,
Multisensor data fusion.
URI
http://etd.lib.metu.edu.tr/upload/12621816/index.pdf
https://hdl.handle.net/11511/27065
Collections
Graduate School of Natural and Applied Sciences, Thesis
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T. İnce, “A comparative study on tightly coupled visual aided inertial navigation systems for unmanned aerial vehicles,” M.S. - Master of Science, Middle East Technical University, 2018.