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- © Robotics
Kinematic structures are currently one of the most
important prerequisites for advanced robot technology.
Grasping
objects, painting cars or performing household tasks are just a few
examples from the manifold space of possible applications.
To execute such operations, the structure of an object has to be
knownapriori.In dynamical changing environments, however, this prior
specification becomes unfeasible. Therefore, an automated process is
required that can reliably extract kinematic structures from visual
sensory input.
This work contributes to this effort by
acquiring joint types from moving 3d point clouds. A cloud consists
hereby of presegmented visual features from object parts or the
environment.
The algorithm tries to classify each relationship in
the cloud according to different categories of joint types.
Several real world experiments with ordinary objects like doors,
drawers, tricycles, and laptops were performed, to test the stability
of this approach.
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