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Consistent Monocular Ackermann Visual-Inertial Odometry for Intelligent and Connected Vehicle Localization.

Fangwu Ma1, Jinzhu Shi1, Liang Wu1

  • 1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China.

Sensors (Basel, Switzerland)
|October 14, 2020
PubMed
Summary

This study introduces MAVIO, a visual-inertial odometry system that enhances scale direction observability and positioning accuracy for intelligent vehicles. MAVIO fuses vehicle velocity and yaw rate measurements, improving performance during challenging motions.

Keywords:
intelligent and connected vehicleslever arm effectvehicle velocity and yaw angular rate error measurementsvisual–inertial odometry (VIO)

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Area of Science:

  • Robotics
  • Computer Vision
  • Navigation Systems

Background:

  • Visual-inertial odometry (VIO) systems face challenges with scale direction observability under degenerate vehicle motions.
  • Existing relative kinematic error models assume constant vehicle velocity, degrading positioning accuracy.

Purpose of the Study:

  • To propose a consistent monocular Ackermann VIO (MAVIO) system to improve scale observability and positioning accuracy.
  • To address the limitations of constant velocity assumptions in VIO systems.
  • To integrate vehicle velocity and yaw angular rate error measurements with the lever arm effect.

Main Methods:

  • Developed MAVIO, a tightly coupled filter-based mechanism fusing Ackermann error state measurements.
  • Incorporated the lever arm effect for improved information exchange between vehicle and IMU coordinates.
  • Introduced MAVIO-GNSS by integrating raw Global Navigation Satellite System (GNSS) error measurements.

Main Results:

  • MAVIO significantly improved the observability of the VIO scale direction for ground vehicles.
  • The system resolved inconsistencies in relative kinematic error models, enhancing positioning accuracy.
  • MAVIO-GNSS further boosted vehicle positioning accuracy, especially during long-distance driving.

Conclusions:

  • MAVIO offers improved robustness and accuracy for VIO systems in intelligent vehicles.
  • The proposed methods effectively handle degenerate motions and improve navigation precision.
  • Publicly available source code facilitates community adoption and further research.