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Latency Compensated Visual-Inertial Odometry for Agile Autonomous Flight.
1School of Aerospace Engineering, Georgia Institute of Technology, 270 Ferst Drive, Atlanta, GA 30313, USA.
Sensors (Basel, Switzerland)
|April 17, 2020
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.
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.
Keywords:
EKFIMUUAVVIOcamera visionlatency compensationnavigationonline temporal calibrationsensor fusiontime delayMore Related Videos
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