Localization methods for a mobile robot in urban environments

This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on robotics Jg. 20; H. 5; S. 851 - 864
Hauptverfasser: Georgiev, A., Allen, P.K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.10.2004
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Applied sciences
Buildings
Cameras
Computer science; control theory; systems
Control theory. Systems
Exact sciences and technology
Global Positioning System
Localization method
Mathematical models
Mobile robots
Navigation
Navigation systems
Photometry
Position (location)
Robot sensing systems
Robot vision systems
Robotics
Robotics and automation
Robots
Sensors
Uncertainty
Urban areas
Tilt angle
Computer vision
Urban environment
Navigation
Image matching
Moving robot
Linear estimation
Localization
Extended Kalman filter
Robotics
ISSN:1552-3098, 1941-0468
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2004.829506