Paper Info
Title
Vision-aided inertial navigation on an uncertain map using a particle filter
Abstract
This paper presents a vision-based navigation solution for unmanned aircraft operations on airfield surfaces in GPS-denied environments. The Unmanned Aircraft System Ground Operations Management System (UGOMS) described here combines measurements from a computer vision system and inertial sensors with an airport layout database to provide real-time position determination on the airfield surface. UGOMS provides both absolute position of the aircraft as well as relative position to airport surface elements such as runway hold lines and taxiway edges. The key technical components of UGOMS are computer vision algorithms that classify image regions, Markov localization using particle filters, and a navigation architecture which incorporates the localization information. An overview of the overall UGOMS architecture is presented as well as preliminary test results using an uncertain airfield map to highlight the performance capabilities of the system.
Year
DOI
Venue
2009
10.1109/ROBOT.2009.5152778
ICRA
Keywords
Field
DocType
Markov processes,computer vision,image classification,inertial navigation,remotely operated vehicles,sensors,Markov localization,Unmanned Aircraft System Ground Operations Management System,airport layout database,airport surface elements,computer vision system,image classification,inertial sensors,particle filter,real-time position determination,uncertain airfield map,vision-aided inertial navigation
Inertial navigation system,Remotely operated underwater vehicle,Computer vision,Markov process,Particle filter,Markov chain,Artificial intelligence,Inertial measurement unit,Engineering,Runway,Contextual image classification
Conference
Volume
Issue
ISSN
2009
1
1050-4729
Citations 
PageRank 
References 
3
0.49
8
Authors
4
Name
Order
Citations
PageRank
Jason Durrie130.49
Tristan Gerritsen230.49
Eric W. Frew318226.73
Stephen Pledgie430.49