A Hardware-Minimal Unscented Kalman Filter Framework for Visual-Inertial Navigation of Small Unmanned Aircraft

This thesis presents the development and implementation of a software framework for estimating the position of a drone during flight. This framework is based on an algorithm known as the Unscented Kalman Filter (UKF), a recursive method of estimating the state of a highly nonlinear system, such as an aircraft. In this thesis, we present a UKF formulation specially designed for a quadcopter carrying an Inertial Measurement Unit (IMU) and a downward-facing camera. The UKF fuses data from each of these sensors to track the position of the quadcopter over time. This work supports a number of similar efforts in the robotics and aerospace communities to navigate in GPS-denied environments with minimal hardware and minimal computational complexity. The software framework explored in this thesis provides a means for roboticists to easily implement similar UKF-based state estimators for a wide variety of systems, including surface vessels, undersea vehicles, and automobiles. We test the system's effectiveness by comparing its position estimates to those of a commercial motion capture system and then discuss possible applications.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2017

Author dc:creator

• Eddy, Joshua Galen

Chair dc:contributor.committeechair

• Kochersberger, Kevin B.

Committee members dc:contributor.committeemember

• Farhood, Mazen H.
• Woolsey, Craig A.

• Unscented Kalman Filter
• SLAM
• State Estimation
• Localization
• Visual-Inertial Navigation
• Unmanned Aircraft

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:12249

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/77927