Theory of Robot Control
Digital Image Processing
A Functional Architecture for a Team of Fully Autonomous Cooperative Robots
RoboCup-99: Robot Soccer World Cup III
Self-Localization in the RoboCup Environment
RoboCup-99: Robot Soccer World Cup III
RoboCup 2001: Robot Soccer World Cup V
A Two-Tiered Approach to Self-Localization
RoboCup 2001: Robot Soccer World Cup V
RoboCup 2000: Robot Soccer World Cup IV
A Non-traditional Omnidirectional Vision System with Stereo Capabilities for Autonomous Robots
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Multi-sensor Navigation for Soccer Robots
RoboCup 2001: Robot Soccer World Cup V
Multi-cue Localization for Soccer Playing Humanoid Robots
RoboCup 2006: Robot Soccer World Cup X
A Novel Approach to Efficient Monte-Carlo Localization in RoboCup
RoboCup 2006: Robot Soccer World Cup X
Landmark based global self-localization of mobile soccer robots
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
A survey on team strategies in robot soccer: team strategies and role description
Artificial Intelligence Review
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In this paper, a method for robot self-localization based on a catadioptric omni-directional sensor is introduced. The method was designed to be applied to fully autonomous soccer robots participating in the middle-size league of RoboCup competitions. It uses natural landmarks of the soccer field, such as field lines and goals, as well as a priori knowledge of the field geometry, to determine the robot position and orientation with respect to a coordinate system whose location is known. The landmarks are processed from an image taken by an omni-directional vision system, based on a camera plus a convex mirror designed to obtain (by hardware) the ground plane bird's eye view, thus preserving field geometry in the image. Results concerning the method's accuracy are presented.