Robust Monte Carlo Localization for Mobile Robots

Abstract

Mobile robot localization is the problem of determining a robot's pose from sensor data. Monte Carlo Localization is a family of algorithms for localization based on particle filters, which are approximate Bayes filters that use random samples for posterior estimation. Recently, they have been applied with great success for robot localization. Unfortunately, regular particle filters perform poorly in certain situations. Mixture-MCL, the algorithm described here, overcomes these problems by using a "dual" sampler, integrating two complimentary ways of generating samples in the estimation. To apply this algorithm for mobile robot localization, a kd-tree is learned from data that permits fast dual sampling. Systematic empirical results obtained using data collected in crowded public places illustrate superior performance, robustness, and efficiency, when compared to other state-of-the-art localization algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2000
Accession Number
ADA376945

Entities

People

  • Dieter Fox
  • Frank Dellaert
  • Sebastian Thrun
  • Wolfram Burgard

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Bayes Filters
  • Computer Graphics
  • Computer Science
  • Coordinate Systems
  • Data Sets
  • Diagrams
  • Equations
  • Grids
  • Probabilistic Models
  • Probability
  • Range Finders
  • Sampling
  • Sequential Monte Carlo Methods
  • Simulations
  • Statistical Samples
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Robotics and Automation.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy