Robert Eidenberger, Raoul Zoellner, Wendelin Feiten, Thilo Grundmann,
"Fast Parametric Viewpoint Estimation for Active Object Detection"
: Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008
Original Titel:
Fast Parametric Viewpoint Estimation for Active Object Detection
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Original Kurzfassung:
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.
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