An Online Mapping Algorithm for Teams of Mobile Robots

Abstract

We propose a new probabilistic algorithm for online mapping of unknown environments with teams of robots. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an online algorithm that can cope with large odometric errors typically found when mapping an environment with cycles. The algorithm can be implemented distributedly on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Finally, an extension is described for acquiring three-dimensional maps, which capture the structure and visual appearance of indoor environments in 3D.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2000
Accession Number
ADA385127

Entities

People

  • Sebastian Thrun

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayes Filters
  • Computations
  • Computer Programs
  • Coordinate Systems
  • Data Association
  • Estimators
  • Measurement
  • Probability
  • Range Finders
  • Robot Mapping
  • Robotics
  • Sequential Monte Carlo Methods
  • Simultaneous Localization And Mapping
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Geodesy
  • Robotics and Automation.

Technology Areas

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