Paper Info
Title
Real-Time Neural Network Based Camera Localization And Its Extension To Mobile Robot Control
Abstract
The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models.
Year
DOI
Venue
1997
10.1142/S012906579700029X
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Field
DocType
Volume
Mobile robot control,Computer vision,Computer science,Systems modeling,Artificial intelligence,Analytic solution,Artificial neural network,Multiple sensors,Mobile robot,Calibration
Journal
8
Issue
ISSN
Citations 
3
0129-0657
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Doo-Hyun Choi16512.25
Oh Se-young2275.16