Abstract
In this article, the extrinsic parameter estimation of a camera mounted on an intelligent vehicle is addressed. The trifocal tensor is utilized to construct vision dynamics which relates image coordinates, velocity signals, and extrinsic parameters. Artificial visual patterns such as chessboards and planar reference objects used in homography-based methods are no longer required. An auxiliary tensor decouples the rotational extrinsic parameters from the translational ones. A key frame strategy is adopted to deal with the field of view constraint and an unknown distance is eliminated from the vision dynamics to counter the scale change caused by key frame switching. The Lyapunov method is used to design nonlinear observers, which estimate the extrinsic parameters at each time step based all collected valid historical data. Performance of the proposed method is verified by both simulation and experimental results.
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