Localization Methods for a Mobile Robot in Urban Environments

Abstract

This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments.

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Document Details

Document Type
Technical Report
Publication Date
Oct 04, 2004
Accession Number
AD1021293

Entities

People

  • Atanas Georgiev
  • Peter K. Allen

Organizations

  • Columbia University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Autonomous Navigation
  • Computations
  • Computer Vision
  • Coordinate Systems
  • Dead Reckoning
  • Detectors
  • Image Processing
  • Kalman Filters
  • Measurement
  • Motion Planning
  • Navigation
  • Orientation (Direction)
  • Robots
  • Three Dimensional
  • Two Dimensional
  • Urban Areas

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Robotics and Automation.

Technology Areas

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