Type of Document Master's Thesis Author Sharkasi, Adam Tawfik Author's Email Address email@example.com URN etd-05062008-112710 Title Stereo Vision Based Aerial Mapping Using GPS and Inertial Sensors Degree Master of Science Department Mechanical Engineering Advisory Committee
Advisor Name Title Kochersberger, Kevin Bruce Committee Chair Rhody, Harvey Committee Member Wicks, Alfred L. Committee Member Keywords
- Stereo Vision
- Unmanned Systems
- 3D Mapping
- Inertial Measurement
Date of Defense 2008-04-30 Availability unrestricted AbstractThe robotics field has grown in recent years to a point where unmanned systems are no
longer limited by their capabilities. As such, the mission profiles for unmanned systems
are becoming more and more complicated, and a demand has risen for the deployment of
unmanned systems into the most complex of environments. Additionally, the objectives
for unmanned systems are once more complicated by the necessity for beyond line of
sight teleoperation, and in some cases complete vehicle autonomy.
Such systems require adequate sensory devices for appropriate situational awareness.
Additionally, a large majority of what is currently being done with unmanned systems
requires visual data acquisition. A stereo vision system is ideal for such missions as it
doubles as both an image acquisition device, and a range finding device. The 2D images
captured with a stereo vision system can be mapped to three dimensional point clouds
with reference to the optic center of one of the stereo cameras. While stand alone
commercial stereo vision systems are capable of doing just that, the GPS/INS aided
stereo vision system also has integrated 3-axis accelerometers, 3-axis gyros, 3-axis
magnetometer, and GPS receiver allowing for the measurement of the system’s position
and orientation in global coordinates. This capability provides the potential to georeference
the 3D data captured with the stereo camera.
The GPS/INS aided stereo vision system integrates a combination of commercial and inhouse
developed devices. The total system includes a Point Grey Research Bumblebee
stereovision camera, a Versalogic PC104 computer, a PCB designed for sensor
acquisition and power considerations, and a self contained battery. The entire system is
all contained within a 9.5” x 5” x 6.5” aluminum enclosure and weighs approximately 6
lbs. The system is also accompanied with a graphical user interface which displays the
geo-referenced data within a 3D virtual environment providing adequate sensor feedback
for a teleoperated unmanned vehicle.
This thesis details the design and implementation of the hardware and software included
within this system as well as the results of operation.
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