Abstract
In this paper we present a framew rk to recognize objects and to determine their pose from a set of bjects in a scene for automatic manipulation (bin picking) using pixel-synchronous range and intensity images. The approach uses three-dimensional bject models. The object identification and pose estimation process is structured into three stages. The first stage is the feature collection stage, where the feature detection is performed in an area of interest followed by the hypothesis generation which tries to form hypotheses from consistent features. The last stage, the hypothesis verification, tries to evaluate the hypotheses by comparing the measured range data to the predicted range data from hypothesis and the model.
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Hohnhaeuser, B., Hommel, G. (2001). Object Identification and Pose Estimation for Automatic Manipulation. In: Klette, R., Peleg, S., Sommer, G. (eds) Robot Vision. RobVis 2001. Lecture Notes in Computer Science, vol 1998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44690-7_7
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DOI: https://doi.org/10.1007/3-540-44690-7_7
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