Nitendra Nath, David Braganza, Darren M. Dawson, and Timothy Burg
Range identiļ¬cation, least-squares estimation, vision-based systems, nonlinear systems
In this paper, a new estimator using a single calibrated camera mounted on a moving platform is developed to asymptotically recover the range and the three-dimensional (3D) Euclidean position of a static object feature. The estimator also recovers the constant 3D Euclidean coordinates of the feature relative to the world frame as a by-product. The position and orientation of the camera is assumed to be measurable unlike existing observers where velocity measurements are assumed to be known. To estimate the unknown range variable, an adaptive least-squares estimation strategy is employed based on a novel prediction error formulation. A Lyapunov stability analysis is used to prove the convergence properties of the estimator.
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