Application of Competitive Associative Nets to Plane Extraction from Range Data

This article describes an application of competitive associative net called CAN2 to plane extraction from 3D range images measured by a laser range finer (LRF). The CAN2 basically is a neural net which learns efficient piecewise linear approximation of nonlinear functions, and in this application it is utilized for learning piecewise planner (linear) surfaces from the range data. As a result of the learning, the obtained piecewise planner surfaces are more precise than the actual planner surfaces, so that we introduce a method to gather piecewise planner surfaces for reconstructing the actual planner surfaces. We apply the method to the real range data, and examine the effectiveness and the performance.