It is advantageous to use an independently mobile camera in visually servoed disassembly operations since its degrees of freedom can be used to bring the camera into an optimal view-pose. Apart from the vision-based closed-loop control that guides the disassembly robot, a second vision-based closed-loop control can be set up which upgrades the camera from a passive to an active sensor and optimizes the view-pose according to changes in the scene domain. The objective of this contribution is to present an approach to model-based pose initialization and pose refinement steps of an active camera which is able to simultaneously observe and track several objects moving to some extent independently from each other. In both steps, constraints regarding object occlusions, the limited field-of-view, and the uncertainty to pose estimation are exploited to determine an optimal camera pose. The feasibility of this approach to model-based pose initialization is experimentally validated and document ed for the case of the manipulation of a shock absorber lid
This contribution describes an extensively tested approach to three-dimensional tracking of a known polyhedral object at two levels of visual robot control, a position-based tracking level and a velocity-based tracking level. At tile higher position-based level, an iterated extended KalmamFilter is used to track a workpiece at a control-cycle of about two hundred milliseconds. This slower cycle operates on top of the video-rate tracking-loop of the velocity-based tracking level. While features such as vertices, edges, and ellipses extracted from gray-value images are used to adjust the 3D pose-estimate at the higher level of control, optical flow is used to initialize the velocity parameters of the upper level and to locally update poses at the lower levet.
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