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arXiv Computer Vision By R. Yang, Y. Bar-Shalom, H. A. J. Huang

Camera Calibration Using Inaccurate and Asynchronous Discrete GPS Trajectory from Drones

Read the original on arXiv Computer Vision →

The paper tackles the problem of calibrating a stationary camera’s yaw, pitch, and roll using a drone’s GPS trajectory, which suffers from altitude bias, time offset, and discrete sampling. It formulates a parameter estimation problem that jointly estimates the GPS altitude bias, time offset, and camera orientation biases, and proposes a maximum likelihood estimator based on Iterated Least Squares to handle the asynchronous, discrete GPS data. Simulation results show the estimator achieves accuracy close to the Cramér–Rao Lower Bound, with a recommended drone trajectory yielding calibration errors within 14% of the measurement error standard deviation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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