• Corpus ID: 2092191

KLD-Sampling: Adaptive Particle Filters and Mobile Robot Localization

@inproceedings{Fox2001KLDSamplingAP,
  title={KLD-Sampling: Adaptive Particle Filters and Mobile Robot Localization},
  author={Dieter Fox},
  booktitle={NIPS 2001},
  year={2001}
}
  • D. Fox
  • Published in NIPS 2001
  • Computer Science
We present a statistical approach to adapting the sample set size of particle filters on-thefly. [] Key Result Extensive experiments using mobile robot localization as a test application show that our approach yields drastic imp rovements over particle filters with fixed sample set sizes and over a previously introduced adapt tion technique.
cs.washington.edu
89 Citations
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29
Methods Citations
40
Results Citations
2

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References

SHOWING 1-10 OF 30 REFERENCES
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.
Tracking multiple moving targets with a mobile robot using particle filters and statistical data association
TLDR
A sample-based variant of joint probabilistic data association filters is introduced to track features originating from individual objects and to solve the correspondence problem between the detected features and the filters.
Feature based CONDENSATION for mobile robot localization
  • P. Jensfelt, D. Austin, O. Wijk, M. Andersson
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
TLDR
This paper presents a comparison of four different feature types: sonar based triangulation points and point pairs, as well as lines and doors extracted using a laser scanner, and some general guidelines are drawn for determining good feature types.
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.
Using the CONDENSATION algorithm for robust, vision-based mobile robot localization
TLDR
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. Lenser, M. Veloso
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
TLDR
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.
Particle Filters for Mobile Robot Localization
This chapter investigates the utility of particle filters in the context of mobile robotics. In particular, we report results of applying particle filters to the problem of mobile robot localization,…
Navigating Mobile Robots: Systems and Techniques
This is a survey of the state-of-the-art in sensors, systems, methods and technologies utilized by a mobile robot to determine its position in the environment. The many potential "solutions" are…
Filtering via Simulation: Auxiliary Particle Filters
This article analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not robust to outliers for two reasons: the design of the simulators and the…
Particle methods for Bayesian modeling and enhancement of speech signals
TLDR
On-line estimation of the clean speech and model parameters and the adequacy of the chosen statistical models are performed and it is shown how model adequacy may be determined by combining the particle filter with frequentist methods.
...
...