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Towards Eliminating Manual Color Calibration at RoboCup

  • Mohan Sridharan
  • Peter Stone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)

Abstract

Color calibration is a time-consuming, and therefore costly requirement for most robot teams at RoboCup. This paper presents an approach for autonomous color learning on-board a mobile robot with limited computational and memory resources. It works without any labeled training data and trains autonomously from a color-coded map of its environment. The process is fully implemented, completely autonomous, and provides high degree of segmentation accuracy. Most importantly, it dramatically reduces the time needed to train a color map in a new environment.

Keywords

Mobile Robot Segmentation Accuracy Marker Color Legged Robot Color Label 
These 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mohan Sridharan
    • 1
  • Peter Stone
    • 2
  1. 1.Electrical and Computer EngineeringThe University of Texas at Austin 
  2. 2.Department of Computer SciencesThe University of Texas at Austin 

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