Abstract
This paper presents a general and flexible knowledge representation system using attributed graph representation (AGR) and attributed hypergraph representation (AHR) as the basic data structure. Based on these representations, object recognition and interpretation can be achieved by a hypergraph monomorphism algorithm and a knowledge directed search procedure. A graph synthesis procedure is used to combine the AGR’s or AHR’s obtained from images of different views of an object into a unique AHR. For recognition and location of 3-D objects in 2-D perspective images, another form of AHR, known as Point Feature Hypergraph Representation (PHR) is introduced. With PHR, a constellation matching algorithm can be used to compare images and models as well as to derive 3-D information from stereoscopic images. From the PHR of 3-D objects, procedural knowledge can be formulated and used to search for features in a 2-D perspective image for the recognition and location of 3-D objects in 2-D images. Further, the AGR can also be used to represent the geometric and topological information of the world environment of a mobile robot. A special search algorithm converts the AGR into a AHR from which a compact road map is derived for path and trajectory planning as well as navigation. The proposed method renders greater tolerance to local scene changes.
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Wong, A.K.C. (1987). Knowledge Representation for Robot Vision and Path Planning Using Attributed Graphs and Hypergraphs. In: Wong, A.K.C., Pugh, A. (eds) Machine Intelligence and Knowledge Engineering for Robotic Applications. NATO ASI Series, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-87387-4_5
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DOI: https://doi.org/10.1007/978-3-642-87387-4_5
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