• Corpus ID: 36224868

Approaches to Mobile Robot Localization in Indoor Environments

@inproceedings{Jensfelt2001ApproachesTM,
  title={Approaches to Mobile Robot Localization in Indoor Environments},
  author={Patric Jensfelt},
  year={2001}
}
This thesis deals with all aspects of mobile robot localization for indoor applications. The problems span from tracking the position given an initial estimate, over finding it without any prior position knowledge, to automatically building a representation of the environment while performing localization. The theme is the use of minimalistic models which capture the large scale structures of the environment, such as the dominant walls, to provide scalable and low-complexity solutions. In many… 
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References

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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.
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.
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TLDR
It is experimentally shown that minimalistic environmental models can be used to solve the localization problem for a mobile robot in an indoor, structured and non-engineered environment, contrary to many other approaches where a higher degree of detail is used.
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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.
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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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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.
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