C4.5: programs for machine learning
C4.5: programs for machine learning
Fault-Tolerant Self Localization by Case-Based Reasoning
RoboCup 2000: Robot Soccer World Cup IV
Vision-Based Localization in RoboCup Environments
RoboCup 2000: Robot Soccer World Cup IV
Active mobile robot localization
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Quadruped Robot Navigation Considering the Observational Cost
RoboCup 2001: Robot Soccer World Cup V
BabyTigers 2001: Osaka Legged Robot Team
RoboCup 2001: Robot Soccer World Cup V
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Self localization seems necessary for mobile robot navigation. The conventional method such as geometric reconstruction from landmark observations is generally time-consuming and prone to errors. This paper proposes a method which constructs a decision tree and prediction trees of the landmark appearance that enable a mobile robot with a limited visual angle to observe efficiently and make decisions without global positioning in the environment. By constructing these trees based on information criterion, the robot can accomplish the given task efficiently. The validity of the method is shown with a four legged robot.