RESUMO
This paper proposes a new approach to estimate the camera displacement of stereo vision systems via minimization of the algebraic error over the essential matrices manifold. The proposed approach is based on the use of homogeneous forms and linear matrix inequality (LMI) optimizations, and has the advantages of not presenting local minima and not introducing approximations of nonlinear terms. Numerical investigations carried out with both synthetic and real data show that the proposed approach provides significantly better results than SVD methods as well as minimizations of the algebraic error over the essential matrices manifold via both gradient descent and simplex search algorithms.
Assuntos
Artefatos , Inteligência Artificial , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão/métodos , Fotogrametria/métodos , Algoritmos , Fotogrametria/instrumentação , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
The problem of evaluating worst-case camera positioning error induced by unknown-but-bounded (UBB) image noise for a given object-camera configuration is considered. Specifically, it is shown that upper bounds to the rotation and translation worst-case error for a certain image noise intensity can be obtained through convex optimizations. These upper bounds, contrary to lower bounds provided by standard optimization tools, allow one to design robust visual servo systems.
RESUMO
A simple technique for estimating the camera displacement from point correspondences in eye-in-hand visual servoing is presented. The idea for providing more accurate results than existing methods consists of taking into account that the point correspondences used during the camera motion correspond to stationary spatial points, hence exploiting additional information. This is done by first estimating the object Euclidean structure and then estimating the camera displacement from this estimate.