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Online IMU Self-Calibration for Visual-Inertial Systems.

Yao Xiao1, Xiaogang Ruan2, Jie Chai3

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China. xiaoyao1103@emails.bjut.edu.cn.

Sensors (Basel, Switzerland)
|April 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an online self-calibration method for low-cost inertial measurement units (IMUs) in visual-inertial systems. The approach improves 3D pose estimation accuracy without external equipment by jointly optimizing IMU parameters and system states.

Keywords:
IMU calibrationSLAMsensor fusionvisual odometryvisual-inertial system

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Low-cost microelectromechanical systems (MEMS)-based inertial measurement units (IMUs) suffer from inherent errors like inaccurate scale factors, axis misalignments, and g-sensitivity.
  • These errors significantly degrade the performance of visual-inertial systems (VINS), particularly in applications requiring precise 3D pose estimation.
  • Existing calibration methods often require external equipment or offline processing, limiting their applicability in dynamic or unknown environments.

Purpose of the Study:

  • To develop an online self-calibration method for IMUs integrated into visual-inertial systems.
  • To enable concurrent 3D pose estimation and IMU intrinsic parameter calibration without external aiding.
  • To enhance the accuracy and robustness of VINS using low-cost inertial sensors in unknown environments.

Main Methods:

  • A novel preintegration method was developed to accurately propagate IMU intrinsic parameter errors.
  • The IMU calibration problem was formulated as general factors for seamless integration into graph-based VINS frameworks.
  • A joint optimization approach was employed to simultaneously estimate system states and IMU intrinsic parameters within a large bundle adjustment framework.

Main Results:

  • The proposed method successfully calibrated IMU intrinsic parameters online and in real-time.
  • Experimental evaluation on a public dataset demonstrated significant improvements in VINS estimation precision.
  • The approach outperformed traditional methods relying on offline precalibrated IMU measurements.

Conclusions:

  • The developed online IMU self-calibration method effectively addresses calibration challenges in low-cost IMUs for VINS.
  • The joint optimization strategy enhances the accuracy and reliability of visual-inertial odometry in unknown environments.
  • This work provides a practical solution for improving the performance of VINS without the need for specialized calibration equipment.