Robust Visual Servoing  

Oleh:

Kragic, D. ; Christensen, H. I.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The International Journal of Robotics Research vol. 22 no. 10-11 (Oct. 2003), page 923-939.
Topik: visual servoing; grasping; cue integration; robustness; integration
Fulltext: 923.pdf (338.72KB)

Isi artikel

For service robots operating in domestic environments, it is not enough to consider only control level robustness; it is equally important to consider how image information that serves as input to the control process can be used so as to achieve robust and efficient control. In this paper we present an effort towards the development of robust visual techniques used to guide robots in various tasks. Given a task at hand, we argue that different levels of complexity should be considered; this also defines the choice of the visual technique used to provide the necessary feedback information. We concentrate on visual feedback estimation where we investigate both two- and three-dimensional techniques. In the former case, we are interested in providing coarse information about the object position/velocity in the image plane. In particular, a set of simple visual features (cues) is employed in an integrated framework where voting is used for fusing the responses from individual cues. The experimental evaluation shows the system performance for three different cases of camera–robot configurations most common for robotic systems. For cases where the robot is supposed to grasp the object, a twodimensional position estimate is often not enough. Complete pose (position and orientation) of the object may be required. Therefore, we present a model-based system where a wire-frame model of the object is used to estimate its pose. Since a number of similar systems have been proposed in the literature, we concentrate on the particular part of the system usually neglected—automatic pose initialization. Finally, we show how a number of existing approaches can successfully be integrated in a system that is able to recognize and grasp fairly textured, everyday objects. One of the examples presented in the experimental section shows a mobile robot performing tasks in a real-word environment—a living room.

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