A comparison of four fast vision based object recognition methods for programming by demonstration applications

Service robots require interactive programming interfaces that allow users without programming experience to easily instruct the robots. Systems following the programming-by-demonstration (PbD) paradigm are getting closer to this goal. Visual observation of the user and environment is one important aspect for reasoning about goals and actions. With respect to the automatic generation of adaptive programs, a PbD-System should detect, classify and determine the pose of manipulable objects in a fast and stable way. This paper presents a comparison of four established methods proposing a new object classification approach that combines these methods. This gives a means for setting up a world model of a manipulations scene automatically and initializes active contour parameters in order to trade motions of these objects.

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