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Master of Science Thesis Defense Presentation This Afternoon


Posted 12/17/2018
Submitted by Katrina Mill
Category: Campus Announcements, Student Announcements

Thesis Title: Benchmarking of Visual Tracking Algorithms during Rapid UAV Motion

Student: Eric Jacobson

Degree: Master of Science

Dept.:   Mechanical Engineering

Research Group: Autonomous Mobile Robotics Research Group

Date:    Tuesday, December 18th

Time:   2:30 to 4pm

Location: UT 215

 

Chairperson:

Dr. Akin Tatoglu, ME Department

Committee Members:

Dr. Eoin King, ME Department

Dr. Kiwon Sohn, ECE Department

ABSTRACT

Autonomous mobile robots including unmanned aerial vehicles (UAV) fuse camera and inertial measurement unit data for perception, localization and mapping purposes. Localization algorithms require to identify a visual feature and to register the same feature in the next camera frame. The performance of landmark tracking depends on the processing time and total displacement of the robot between frames. If the robot has an agile motion plan –such as an emergency response robot or a co-robot at a human occupied environment— the physical displacement will be greater which will minimize the allowed processing time due to increased search area.

There are distinct problems when implementing such tracking algorithms. During rapid motion, the variables including viewing angle, orientation, distance and ambient lighting conditions will vary substantially. The goal of this thesis is to find out how well the gradient based and more advanced landmark detection and tracking algorithms work under highly dynamic conditions. This means to figure out what will happen if the UAV/camera is moved rapidly in an unexplored environment without any known landmarks or a priori map.

In order to simulate rapid drone motion, a robotic arm assembly and a simulated outdoor environment was created and used as the experimental setup to collect images. Mathematical background, robotic arm control logic, sensor fusion techniques, the experimental results of ten different motion profiles and ambient lighting conditions are plotted and compared with total 201 plots. It is shown that based on the path planning algorithm, a UAV can optimize and switch between algorithms with respect to the controller input.

 

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