Abstract
Global vision systems as found in the small size league are prohibited in the middle size league. This paper presents methods for creating a global view of the world by cooperative sensing of 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 for robots participating in the middle-size league and compare them to a simple averaging method. Results including situations from real competition games are presented.KeywordsMobile RobotData AssociationSensor FusionBall PositionWorld ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
