A generic framework that employs a sensor-independent, feature-based relational model, called the geometric feature relation graph (GFRG), to represent information acquired by various sensors is proposed. A GFRG consists of nodes representing 3-D geometric features and arcs denoting spatial relations between features. Sensor fusion is then accomplished by integrating multiple irregular GFRGs constructed by various sensors into a regular GFRG. A procedure is presented for identifying corresponding measurements of features in the presence of sensory uncertainty with geometric and topological constraints, and a nonlinear programming formulation for maintaining consistency in a network of relations is proposed. The Dempster-Shafer theory of belief functions is applied to make topological constraints in achieving reliable identification. Optimal and heuristic solutions for maintaining consistency are presented. The heuristic solution has near-optimal performance with less computational complexity. Computer simulations verify the validity and performance of the framework.
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