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M. Dietl and J.-S. Gutmann and B. Nebel
ABSTRACT
This work presents methods for tracking objects from noisy and unreliable data taken by a team of robots. We develop a multiobject tracking algorithm based on Kalman filtering and a singleobject tracking method involving a combination of Kalman filtering and Markov localization for outlier detection. We apply these methods in the context of robot soccer for robots participating in the middlesize league and compare them to a simple averaging method. Results including situations from real competition games are presented. 
ECVision indexed and annotated bibliography of cognitive computer vision publications
This bibliography was created by Hilary Buxton and Benoit Gaillard, University of Sussex, as part of ECVision Specific Action 8-1
The complete text version of this BibTeX file is available here: ECVision_bibliography.bib
Cooperative Sensing in Dynamic EnvironmentsSite generated on Friday, 06 January 2006