Cooperative sensing in dynamic environments
- 13 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 1706-1713
- https://doi.org/10.1109/iros.2001.977224
Abstract
This work presents methods for tracking objects from noisy and unreliable data taken by a team of robots. We develop a multi-object tracking algorithm based on Kalman filtering and a single-object 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 RoboCup middle-size league and compare them to a simple averaging method. Results including situations from real competition games are presented.Keywords
This publication has 17 references indexed in Scilit:
- High resolution maps from wide angle sonarPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Tracking multiple moving targets with a mobile robot using particle filters and statistical data associationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Distributed sensor fusion for object position estimation by multi-robot systemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Fast and robust tracking of multiple moving objects with a laser range finderPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- AGILO RoboCuppers 2001: Utility- and Plan-Based Action Selection Based on Probabilistically Estimated Game SituationsPublished by Springer Nature ,2002
- CoPS-Team DescriptionPublished by Springer Nature ,2000
- Mobile robot sense netPublished by SPIE-Intl Soc Optical Eng ,1999
- RoboCupPublished by Association for Computing Machinery (ACM) ,1997
- A review of statistical data association techniques for motion correspondenceInternational Journal of Computer Vision, 1993
- The Kalman Filter: An Introduction to ConceptsPublished by Springer Nature ,1990