A Probabilistic Approach for Concurrent Map Acquisition and Localization for Mobile Robots

Abstract

This paper addresses the problem of building large scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1997
Accession Number
ADA332317

Entities

People

  • Dieter Fox
  • Sebastian Thrun
  • Wolfram Burgard

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Autonomous Navigation
  • Birds
  • Computational Complexity
  • Computations
  • Computer Science
  • Maximum Likelihood Estimation
  • Models
  • Navigation
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Robot Mapping
  • Robots

Fields of Study

  • Computer science
  • Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Robotics and Automation.
  • Statistical inference.

Technology Areas

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