Vision-based Target Geo-location using a Fixed-wing Miniature Air Vehicle

Authors:
D. Blake Barber;Joshua D. Redding;Timothy W. Mclain;Randal W. Beard;Clark N. Taylor
Affiliations:
Magicc Lab, Brigham Young University, Provo, USA 84602;Scientific Systems Company, Inc., Woburn, USA 01801;Magicc Lab, Brigham Young University, Provo, USA 84602;Magicc Lab, Brigham Young University, Provo, USA 84602;Magicc Lab, Brigham Young University, Provo, USA 84602
Venue:
Journal of Intelligent and Robotic Systems
Year:
2006

Citing 0
Cited 5

Visual 3-D SLAM from UAVs

Journal of Intelligent and Robotic Systems
Vision-based unmanned aerial vehicle navigation using geo-referenced information

EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
Multiple targets geolocation using SIFT and stereo vision on airborne video sequences

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Geolocation of Multiple Targets from Airborne Video Without Terrain Data

Journal of Intelligent and Robotic Systems
A Robust Mobile Target Localization Method for Cooperative Unmanned Aerial Vehicles Using Sensor Fusion Quality

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method for determining the GPS location of a ground-based object when imaged from a fixed-wing miniature air vehicle (MAV). Using the pixel location of the target in an image, measurements of MAV position and attitude, and camera pose angles, the target is localized in world coordinates. The main contribution of this paper is to present four techniques for reducing the localization error. In particular, we discuss RLS filtering, bias estimation, flight path selection, and wind estimation. The localization method has been implemented and flight tested on BYU's MAV testbed and experimental results are presented demonstrating the localization of a target to within 3 m of its known GPS location.