Markov Localization for Mobile Robots in Dynamic Environments

@article{Burgard1999MarkovLF,
  title={Markov Localization for Mobile Robots in Dynamic Environments},
  author={Wolfram Burgard and Dieter Fox and Sebastian Thrun},
  journal={ArXiv},
  year={1999},
  volume={abs/1106.0222}
}
Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate… 
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References

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TLDR
Extensions to Markov localization algorithms enabling them to localize mobile robots even in densely populated environments are proposed and implemented, demonstrating that this approach is able to accurately estimate the robot's position in more than 98% of the cases even in such highly dynamic environments.
Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach
  • W. Burgard, Andrcas Derr, D. Fox, A. Cremers
  • Computer Science
    Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)
  • 1998
TLDR
The dynamic Markov localization technique is presented as a uniform approach to position estimation, able to globally estimate the position of the robot, to efficiently track its position whenever the robot's certainty is high, and to detect and recover from localization failures.
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TLDR
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TLDR
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TLDR
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