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High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles.

Eduardo Sánchez Morales1, Julian Dauth1, Bertold Huber2

  • 1Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany.

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Summary

This study introduces a new method to improve autonomous vehicle navigation when satellite signals are lost. It enhances the accuracy of vehicle state estimation using machine learning and LiDAR, reducing reliance on Global Navigation Satellite Systems (GNSS).

Keywords:
Autonomous VehiclesInertial Navigation SystemReal-Time KinematicSatellite Navigationindoor navigationmachine learningreference state

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Area of Science:

  • Automotive Engineering
  • Robotics
  • Navigation Systems

Background:

  • Autonomous driving requires precise vehicle state estimation for testing and validation.
  • Global Navigation Satellite Systems (GNSS) offer practical and accurate positioning but are susceptible to signal loss.
  • Inertial Navigation Systems (INSs) are crucial but often heavily depend on GNSS availability.

Purpose of the Study:

  • To develop a robust methodology for autonomous vehicle navigation that minimizes reliance on GNSS.
  • To enhance the accuracy and reliability of vehicle state estimation in challenging environments with intermittent satellite signals.
  • To introduce a novel LiDAR-based Positioning Method (LbPM) for indoor navigation and complement existing systems.

Main Methods:

  • Implemented machine learning for standstill recognition.
  • Developed a mathematical model for horizontation of inertial measurements.
  • Utilized statistical filtering for sensor fusion and incorporated outlier and drift detection.
  • Introduced a novel LiDAR-based Positioning Method (LbPM) for indoor navigation.

Main Results:

  • Demonstrated significant improvement in vehicle state estimation accuracy under adverse conditions, including corrupted or absent correction data and sensor drift.
  • Validated the methodology's robustness and accuracy against a state-of-the-art INS with Real-Time Kinematic (RTK) correction.
  • Achieved accuracy with the proposed LbPM method comparable to systems utilizing RTK correction.

Conclusions:

  • The proposed methodology effectively reduces dependency on GNSS for autonomous vehicle navigation.
  • The system provides reliable and accurate vehicle state estimation even with degraded or unavailable satellite signals.
  • The novel LbPM offers a viable solution for accurate indoor navigation, complementing outdoor GNSS-based systems.