Abstract:
The aim of this thesis is to develop a system which would enable a quadrotor MAV (Micro Aerial
Vehicle) to estimate its position and orientation and to autonomously navigate in unknown environments
using vision as the primary source of information. To navigate in three-dimensional space,
an autonomous MAV should not only possess knowledge of its current position and orientation
(pose for short), but also of the world around it. While the former can be obtained using a GPS for
large outdoor environments and the latter can be provided as a map, a truly autonomous navigation
system should enable an MAV to infer its pose in indoor, GPS-denied environments using only
the on-board sensors. While images from a camera are rich in data, they are devoid of any depth
information. Extracting depth information from a single camera therefore requires the presence of
reference objects with known geometry such as artificial fiducial markers in the field of view, or
state-of-the-art monocular structure from motion techniques.
In this thesis, we will study and develop solutions for environments which the MAV possesses
no a priori information. We will present our modular approach to the problem of autonomous navigation
which comprises of three separate blocks. The first block uses a state-of-the-art monocular
simultaneous localization and mapping algorithm to transform the camera into a real time pose
sensor. The second block uses an extended Kalman filter to refine the pose information from the
camera by fusing it with data from the MAV’s onboard sensors. The third block uses a proportionalintegral-
derivative controller to generate control commands for the MAV.
We implemented our system on a commercially available quadrotor MAV and tested it in real
world scenarios. The accuracy of our system will be compared against a highly accurate motion
capture system and the findings will be presented/analyzed.
Description:
A Thesis
Submitted to the Faculty of Graduate Studies and
Research In Partial Fulfillment of the Requirements
For the Degree of
Master of Applied Science
in Electronic Systems Engineering
University of Regina. viii, 77 p.