A Monte Carlo Algorithm for Multi-Robot Localization

Abstract

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots, using computer vision and laser range finding for detecting each other and estimating each other's relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1999
Accession Number
ADA363774

Entities

People

  • Dieter Fox
  • Hannes Kruppa
  • Sebastian Thrun
  • Wolfram Burgard

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Computer Science
  • Computers
  • Dead Reckoning
  • Equations
  • Estimators
  • Maximum Likelihood Estimation
  • Measurement
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Range Finders
  • Sampling
  • Statistical Algorithms

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy
  • Directed Energy