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Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments.

Mohamed Elkholy1,2, Mohamed Elsheikh1,3, Naser El-Sheimy1

  • 1Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.

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Summary
This summary is machine-generated.

This study presents a novel radar and inertial navigation system (INS) integration with map matching to enhance autonomous vehicle navigation in areas with poor Global Navigation Satellite System (GNSS) signals.

Keywords:
FMCW radarGNSS outagesGNSS-challenging environmentsmap matchingradar odometryradar/INS integrationurban navigationvehicle navigation

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Area of Science:

  • Robotics and Autonomous Systems
  • Navigation and Positioning
  • Sensor Fusion

Background:

  • Global Navigation Satellite System (GNSS) receivers are crucial for autonomous navigation but suffer signal degradation in urban environments.
  • Challenges like signal blockage and multipath effects necessitate complementary sensor technologies.
  • Inertial Navigation Systems (INS) and radar are key alternatives for robust positioning.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for improving land vehicle navigation accuracy in Global Navigation Satellite System (GNSS)-challenged environments.
  • To integrate data from multiple radar units and an Inertial Navigation System (INS) for enhanced positioning.
  • To leverage map matching with OpenStreetMap (OSM) data for real-time position correction.

Main Methods:

  • Utilized four radar units for forward velocity and position estimation.
  • Fused radar data with INS using an extended Kalman filter (EKF).
  • Applied map matching with OpenStreetMap (OSM) to refine the integrated navigation solution.

Main Results:

  • The proposed radar/INS integration with map matching demonstrated significant improvements in navigation accuracy.
  • The algorithm achieved a horizontal position Root Mean Square (RMS) error of less than 1% of the distance traveled.
  • Effective performance was validated during simulated Global Navigation Satellite System (GNSS) outages.

Conclusions:

  • The developed algorithm effectively compensates for Global Navigation Satellite System (GNSS) signal loss in challenging urban areas.
  • Radar/INS integration combined with map matching offers a robust solution for continuous and accurate autonomous vehicle navigation.
  • The method shows high potential for real-world applications requiring reliable positioning.