Abstract
In visual-inertial odometry (VIO), inertial measurement unit (IMU) dead reckoning acts as the dynamic model for flight vehicles while camera vision extracts information about the surrounding environment and determines features or points of interest. With these sensors, the most widely used algorithm for estimating vehicle and feature states for VIO is an extended Kalman filter (EKF). The design of the standard EKF does not inherently allow for time offsets between the timestamps of the IMU and vision data. In fact, sensor-related delays that arise in various realistic conditions are at least partially unknown parameters. A lack of compensation for unknown parameters often leads to a serious impact on the accuracy of VIO systems and systems like them. To compensate for the uncertainties of the unknown time delays, this study incorporates parameter estimation into feature initialization and state estimation. Moreover, computing cross-covariance and estimating delays in online temporal calibration correct residual, Jacobian, and covariance. Results from flight dataset testing validate the improved accuracy of VIO employing latency compensated filtering frameworks. The insights and methods proposed here are ultimately useful in any estimation problem (e.g., multi-sensor fusion scenarios) where compensation for partially unknown time delays can enhance performance.
Highlights
The most widely used algorithms for estimating the states of a dynamic system are a KalmanFilter [1,2] and its nonlinear versions (e.g., extended Kalman filter (EKF) [3,4] and unscented Kalman filter (UKF) [5])
At the measurement update of the EKF, the third correction is to formulate a modified Kalman gain by the cross-covariance term computed during the delay period
The testing results of this study on flight datasets show that the proposed latency compensated visual-inertial odometry (VIO) is a more reliable and accurate navigation solution than the existing VIO systems
Summary
The most widely used algorithms for estimating the states of a dynamic system are a KalmanFilter [1,2] and its nonlinear versions (e.g., extended Kalman filter (EKF) [3,4] and unscented Kalman filter (UKF) [5]). The most widely used algorithms for estimating the states of a dynamic system are a Kalman. The design of the standard Kalman filter does not inherently allow for significant sensor-related delays in computation. As an example of key delay sources, some complex sensors such as vision processors for navigation often require extensive computations to obtain higher-level information from raw sensor data. Delays resulting from heavy computation may distort the quality of state estimation since a current measurement is compared to past states of a system model. Unless compensating delays in Kalman filtering, large estimation errors may accumulate over time, or even cause the filter to diverge
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