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Fast parametric viewpoint estimation for active object detection

Most current solutions to active perception planning struggle with complex state representations or fast and efficient sensor parameter selection strategies. The goal is to find new viewpoints or optimize sensor parameters for further measurements in order to classify an object and precisely locate its position. This paper presents an exclusively parametric approach for the state estimation and decision making process to achieve very low computational complexity and short calculation times. The proposed approach assumes a realistic, high dimensional and continuous state space for the representation of objects expressing their rotation, translation and class. Its probability distribution is described by multivariate mixtures of Gaussians which allow the representation of arbitrary object hypotheses. In a statistical framework Bayesian state estimation updates the current state probability distribution based on a scene observation which depends on the sensor parameters. These are selected in a decision process which aims on reducing the uncertainty in the state distribution. Approximations of information theoretic measurements are used as evaluation criteria.