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Carnegie Mellon Robotics Institute

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Abstract

This paper proposes a statistical method for calibrating the odometry of mobile robots. In contrast to previous approaches, which require explicit measurements of actual motion when calibrating a robot's odometry, the algorithm proposed here uses the robot's sensors to automatically calibrate the robot as it operates. An efficient, incremental maximum likelihood algorithm enables the robot to adapt to changes in its kinematics on-line, as they occur. The appropriateness of the approach is demonstrated in two large-scale environments, where the amount of odometric error is reduced by an order of magnitude.

Notes

Text Reference

Nicholas Roy and Sebastian Thrun, "Online Self-Calibration For Mobile Robots," Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 1999.

BibTeX Reference

@inproceedings{Roy_1999_2831,
   author = "Nicholas Roy and Sebastian Thrun",
   title = "Online Self-Calibration For Mobile Robots",
   booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)",
   year = "1999",
}

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