Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
The RoboCup Physical Agent Challenge: Goals and Protocols for Phase 1
RoboCup-97: Robot Soccer World Cup I
A Segmentation System for Soccer Robot Based on Neural Networks
RoboCup-99: Robot Soccer World Cup III
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
The AGILO autonomous robot soccer team: computational principles, experiences, and perspectives
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
RoboCup 2001: Robot Soccer World Cup V
A Two-Tiered Approach to Self-Localization
RoboCup 2001: Robot Soccer World Cup V
RoboCup-99: Robot Soccer World Cup III
A Localization Method for a Soccer Robot Using a Vision-Based Omni-Directional Sensor
RoboCup 2000: Robot Soccer World Cup IV
From Multiple Images to a Consistent View
RoboCup 2000: Robot Soccer World Cup IV
Optimal estimation of line segments in noisy lidar data
Signal Processing - Signal processing in UWB communications
A Novel Approach to Efficient Monte-Carlo Localization in RoboCup
RoboCup 2006: Robot Soccer World Cup X
Artificial intelligence in robocup
Reasoning, Action and Interaction in AI Theories and Systems
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Knowing the position and orientation of a mobile robot situated in an environment is a critical element for effectively accomplishing complex tasks requiring autonomous navigation. Techniques for robot self-localization have been extensively studied in the past, but an effective general solution does not exist, and it is often necessary to integrate different methods in order to improve the overall result.In this paper we present a self-localization method that is based on the Hough Transform for matching a geometric reference map with a representation of range information acquired by the robot's sensors. The technique is adequate for indoor office-like environments, and specifically for those environments that can be represented by a set of segments. We have implemented and successfully tested this method in the RoboCup environment and we consider this a good benchmark for its use in office-like environments populated with unknown and moving obstacles (e.g. persons moving around).