CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2005 vol.27 Issue No.05 - May
Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms
Issue No.05 - May (2005 vol.27)
Luciano Silva , IEEE
Olga R.P. Bellon , IEEE
Kim L. Boyer , IEEE
This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It requires prealignment of the range surfaces to a reasonable starting point and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hillclimbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GAs are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.
Range image registration, genetic algorithms, robust methods, stochastic search.
Luciano Silva, Olga R.P. Bellon, Kim L. Boyer, "Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.27, no. 5, pp. 762-776, May 2005, doi:10.1109/TPAMI.2005.108