A Robust Method for Registration of Partially-Overlapped Range Images Using Genetic Algorithms
Registration is a fundamental stage in the 3–D reconstruction process. We consider the problem of Euclidean alignment of two arbitrarily-oriented, partially-overlapped surfaces represented by measured point sets contaminated by noise and outliers. Given two approximately aligned range images of a real object, it is possible to carry out the registration of those images using numerous algorithms such as ICP. Basically the task is to match two or more images taken at different times, from different sensors, or from different viewpoints. In this paper, we discuss a number of possible approaches to the registration problem and propose a new method based on the manual pre-alignment of the range images of arbitrarily-oriented surfaces followed by an automatic registration process using a novel genetic optimization algorithm in 3–D data registration. Results for real range data are presented with precision and robustness, combined with the generality of genetic algorithms. This procedure focuses on the problem of obtaining the best correspondence between points through a robust search method between partially overlapped images.
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