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This paper addresses the challenge of calibrating stationary cameras using inaccurate and asynchronous GPS trajectories from drones by formulating the problem as a parameter estimation task. The authors develop a specialized maximum likelihood estimator via the Iterated Least Squares algorithm to simultaneously estimate the GPS altitude bias, time offset, and camera orientation angles. Simulation results indicate that the proposed method achieves calibration accuracy within 14% of the measurement error standard deviation and meets the Cramer-Rao Lower Bound, highlighting its effectiveness in practical applications.
Achieving camera calibration accuracy that meets the Cramer-Rao Lower Bound using flawed and asynchronous GPS data could revolutionize drone-based imaging systems.
This paper considers a stationary camera calibration problem, which estimates the camera orientation angles yaw, pitch and roll, using a drone trajectory recorded by a GPS. There are three challenges in using a GPS trajectory as ground truth for camera calibration. One, the altitude of GPS data is inaccurate with an unknown bias. Two, the GPS receiver and camera are not time synchronized, and there is an unknown time offset between the two systems. Three, the GPS trajectory is time-discrete and accurate interpolation is needed. This is actually an estimation problem since velocity is also needed. To address the first two challenges, we formulate the problem as a parameter estimation problem to estimate a vector consisting of the GPS altitude bias and time offset in addition to the camera yaw, pitch and roll biases. We then develop a special maximum likelihood estimator using the Iterated Least Squares algorithm which can work with a non-synchronized time-discrete GPS trajectory for the third challenge. Since the camera measurement errors are usually small, this requires a high calibration accuracy so that the residual bias error following the calibration should not be significant compared to the measurement error standard deviation. The calibration accuracy depends highly on the drone trajectory. This paper also recommends an appropriate drone trajectory which can yield a good calibration accuracy, namely, 14\% of the measurement error standard deviation. Simulation tests are conducted to demonstrate the algorithm performance. The estimation results meet the Cramer-Rao Lower Bound (CRLB) since the Normalized Estimation Error Squared w.r.t.\ the CRLB is statistically acceptable.
