Markov Localization for Reliable Robot Navigation and People Detection

@inproceedings{Fox1998MarkovLF,
  title={Markov Localization for Reliable Robot Navigation and People Detection},
  author={Dieter Fox and Wolfram Burgard and Sebastian Thrun},
  booktitle={Sensor Based Intelligent Robots},
  year={1998}
}
Localization is one of the fundamental problems in mobile robotics. Without knowledge about their position mobile robots cannot efficiently carry out their tasks. In this paper we present Markov localization as a technique for estimating the position of a mobile robot. The key idea of this technique is to maintain a probability density over the whole state space of the robot within its environment. This way our technique is able to globally localize the robot from scratch and even to recover… 
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References

SHOWING 1-10 OF 42 REFERENCES
Markov Localization for Mobile Robots in Dynamic Environments
TLDR
A version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments, and includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time.
Monte Carlo localization for mobile robots
TLDR
The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
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.
Position Estimation for Mobile Robots in Dynamic Environments
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.
Probabilistic Robot Navigation in Partially Observable Environments
TLDR
First results are reported on first results of a research program that uses par tially observable Markov models to robustly track a robots location in office environments and to direct its goal-oriented actions.
An experimental comparison of localization methods
TLDR
This experimental study compares two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which using Kalman filtering techniques based on matching sensor scans.
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots
TLDR
Monte Carlo Localization is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success and yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches.
Mobile robot localization by tracking geometric beacons
TLDR
An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed.
Hybrid, high-precision localisation for the mail distributing mobile robot system MOPS
  • K. Arras, S. J. Vestli
  • Computer Science
    Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)
  • 1998
TLDR
The new localisation algorithms under implementation for the mail distributing mobile robot, MOPS, of the Institute of Robotics, Swiss Federal Institute of Technology Zurich, employ consistent probabilistic feature extraction, clustering, matching and estimation of the vehicle position and orientation.
AMOS: comparison of scan matching approaches for self-localization in indoor environments
  • J.-S. Gutmann, C. Schlegel
  • Computer Science
    Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96)
  • 1996
TLDR
This paper describes results from evaluating different self-localization approaches in indoor environments for mobile robots based on 2D laser scans and an odometry position estimate and shows that the position error can be kept small enough to perform navigation tasks.
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