Landmark-Aided Localization for Air Vehicles Using Learned Object Detectors
Open Access
- Author:
- DeAngelo, Mark Patrick
- Graduate Program:
- Aerospace Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- August 09, 2016
- Committee Members:
- Joseph F. Horn, Dissertation Advisor/Co-Advisor
Joseph F. Horn, Committee Chair/Co-Chair
Robert G. Melton, Committee Member
Sean N. Brennan, Committee Member
Richard Lee Culver, Outside Member - Keywords:
- localization
feature-based navigation
UAV
UAS
navigation
computer vision
landmark detection
object detection
cascade object detector
bag of words
bag of visual words
bag of keypoints
SVM
visual flight rules
VFR
image processing
UUV
AUV
sonar
underwater
airplane
quadcopter
aircraft
unmanned aerial vehicle
unmanned aerial system
remotely piloted vehicle
RPV
unmanned underwater vehicle
autonomous underwater vehicle
localisation - Abstract:
- This research presents two methods to localize an aircraft without GPS using fixed landmarks observed from an optical sensor. Onboard absolute localization is useful for vehicle navigation free from an external network. The objective is to achieve practical navigation performance using available autopilot hardware and a downward pointing camera. The first method uses computer vision cascade object detectors, which are trained to detect predetermined, distinct landmarks prior to a flight. The first method also concurrently explores aircraft localization using roads between landmark updates. During a flight, the aircraft navigates with attitude, heading, airspeed, and altitude measurements and obtains measurement updates when landmarks are detected. The sensor measurements and landmark coordinates extracted from the aircraft’s camera images are combined into an unscented Kalman filter to obtain an estimate of the aircraft’s position and wind velocities. The second method uses computer vision object detectors to detect abundant generic landmarks referred as buildings, fields, trees, and road intersections from aerial perspectives. Various landmark attributes and spatial relationships to other landmarks are used to help associate observed landmarks with reference landmarks. The computer vision algorithms automatically extract reference landmarks from maps, which are processed offline before a flight. During a flight, the aircraft navigates with attitude, heading, airspeed, and altitude measurements and obtains measurement corrections by processing aerial photos with similar generic landmark detection techniques. The method also combines sensor measurements and landmark coordinates into an unscented Kalman filter to obtain an estimate of the aircraft’s position and wind velocities.