Multiple Representation Approach to Geometric Model Construction From Range Data
A method is presented for constructing geometric design data from noisy 3-D sensor measurements of physical parts. In early processing phase, RLTS regression filters stemming from robust estimation theory are used for separating the desired part of the signal in contaminated sensor data from undesired part. Strategies for producing a complete 3-D data set from partial views are studied. Multiple representations are used in model construction because there is no single representation that would be most appropriate in all situations. In particular, surface triangulation, NURBS, and super-ellipsoids are employed in order to represent efficiently polygonal and irregular shapes, free form surfaces and standard primitive solids. The size of the required control point mesh for spline description is estimated using a surface characterization process. Surfaces of arbitrary topology are modeled using triangulation and trimmed NURBS. A user given tolerance value is driving refinement of the obtained surface model. The resulting model description is a procedural CAD model which can convey structural information in addition to low level geometric primitives. The model is translated to IGES standard product data exchange format to enable data sharing with other processes in concurrent engineering environment. Preliminary results on view registration using simulated data are shown. Examples of model construction using both real and simulated data are also given.