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Latency Compensated Visual-Inertial Odometry for Agile Autonomous Flight.

Kyuman Lee1, Eric N Johnson2

  • 1School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Drive, Atlanta, GA 30313, USA.

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|April 17, 2020
PubMed
Summary

This study enhances visual-inertial odometry (VIO) accuracy by estimating unknown time delays between inertial measurement unit (IMU) and camera data. Compensating for these sensor delays improves state estimation for flight vehicles.

Keywords:
EKFIMUUAVVIOcamera visionlatency compensationnavigationonline temporal calibrationsensor fusiontime delay

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Area of Science:

  • Robotics
  • Computer Vision
  • Sensor Fusion

Background:

  • Visual-inertial odometry (VIO) uses inertial measurement unit (IMU) and camera data for navigation.
  • Extended Kalman Filters (EKFs) are common for VIO state estimation.
  • Uncompensated time delays between IMU and vision data degrade VIO accuracy.

Purpose of the Study:

  • To develop a method for compensating unknown time delays in VIO.
  • To improve the accuracy and robustness of VIO systems.

Main Methods:

  • Incorporated parameter estimation into feature initialization and state estimation.
  • Implemented online temporal calibration to estimate delays, correcting residual, Jacobian, and covariance.
  • Utilized flight dataset testing for validation.

Main Results:

  • Demonstrated improved accuracy in VIO using the proposed latency-compensated filtering framework.
  • Validated the effectiveness of parameter estimation for handling sensor time uncertainties.
  • Showcased the correction of residual, Jacobian, and covariance through online temporal calibration.

Conclusions:

  • The proposed method effectively compensates for partially unknown time delays in VIO.
  • This approach enhances the performance of VIO systems and other multi-sensor fusion applications.
  • Accurate state estimation is achievable even with sensor-related time uncertainties.