Robust Monte Carlo localization for mobile robots

@article{Thrun2001RobustMC,
  title={Robust Monte Carlo localization for mobile robots},
  author={Sebastian Thrun and Dieter Fox and Wolfram Burgard and Frank Dellaert},
  journal={Artif. Intell.},
  year={2001},
  volume={128},
  pages={99-141}
}
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1,998 Citations
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1,998 Citations

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References

SHOWING 1-10 OF 87 REFERENCES

Monte Carlo localization for mobile robots

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.

Active Markov localization for mobile robots

Monte Carlo Localization with Mixture Proposal Distribution

Experimental results with physical robots and an analysis of the formulation of a new proposal distribution for the Monte Carlo sampling step suggest that the new algorithm is significantly more robust and accurate than plain MCL.

Collaborative Multi-Robot Localization

This paper uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion, and illustrates drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

Active global localization for a mobile robot using multiple hypothesis tracking

The method uses multi-hypothesis Kalman filter based pose tracking combined with a probabilistic formulation of hypothesis correctness to generate and track Gaussian pose hypotheses online and generates movement commands for the platform to enhance the gathering of information for the pose estimation process.

An experimental comparison of localization methods

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.

Bayesian estimation and Kalman filtering: a unified framework for mobile robot localization

  • S. RoumeliotisG. Bekey
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
Bayesian hypothesis testing is combined with Kalman filtering to merge two different approaches to map-based mobile robot localization; namely Markov localization and pose tracking.

Using the CONDENSATION algorithm for robust, vision-based mobile robot localization

This paper presents a novel, vision-based localization method based on the CONDENSATION algorithm, a Bayesian filtering method that uses a sampling-based density representation to track the position of the camera platform rather than tracking an object in the scene.

Sensor resetting localization for poorly modelled mobile robots

  • S. LenserM. Veloso
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
A new localization algorithm, called sensor resetting localization, which is an extension of Monte Carlo localization, is presented, which has been successfully used on autonomous legged robots in the Sony legged league of the robotic soccer competition RoboCup'99.

Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids

The position probability grid approach to estimating the robot's absolute position and orientation in a metric model of the environment is described, designed to work with standard sensors and is independent of any knowledge about the starting point.
...