A Novel 3D Registration Algorithm Using Parallel-Light Association
This paper presents a novel method for free-form registration of multiple point clouds. The method adopts a parallel-light data association design inspired from torchlight structure which improves the correctness of point correspondence. When two sets of point clouds are placed together, assume a set of parallel light beams are passing through them. Each light beam will pass the point clouds twice, one on each data set. The Euclidean distance on each light beam between the two sets are taken as measurement of the separation. The fitness is the reciprocal of the mean distance of all light beams. When the two sets are optimally aligned, the fitness is maximized. Hence, the registration problem is reduced to a six degree of freedom search. Preprocessing and acceleration modules such as Genetic Algorithm (GA) are introduced to reduce the exploration space and execution time. Unlike the Iterative Closest Point (ICP) algorithm, the proposed algorithm does not require pre-alignment information. Secondly, ICP does not perform well when the overlapped area between two sets are not sufficiently large. And the proposed algorithm does not suffer from this partial overlapping problem. Based on various experiments with real data, the proposed method has superior performance compared to ICP.
Index Terms3D registration parallel light Iterative Closest Point Genetic Algorithm
Unable to display preview. Download preview PDF.
- 3.Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling, pp. 145–152 (2001)Google Scholar
- 4.Brunnstrom, K., Stoddart, A.J.: Genetic algorithms for free-form surface matching. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 4, pp. 689–693 (1996)Google Scholar
- 5.Silva, L., Bellon, O.R.P., Boyer, K.L.: Enhanced, robust genetic algorithms for multiview range image registration. In: Proceedings of the 4th International Conference on 3-D Digital Imaging and Modeling, pp. 268–275 (2003)Google Scholar
- 9.Liu, R., Burschka, D., Hirzinger, G.: A novel approach to automatic registration of point clouds. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 401–404 (2007)Google Scholar
- 10.Wang, L.R., Xu, F., Hagiwara, I.: An efficient registration algorithm of multi-view three-dimensional images. World Congress on Computer Science and Information Engineering, 703–706 (2009)Google Scholar
- 11.Dai, J.J., Yang, J.: A novel two-stage algorithm for accurate registration of 3-D point clouds. In: International Conference on Multimedia Technology, pp. 6187–6191 (2011)Google Scholar
- 12.Felsberg, M., Larsson, F., Wang, H., Ynnerman, A., Schon, T.B.: Torchlight Navigation. In: Proceedings of the 20th International Conference on Pattern Recognition, pp. 302–306 (2010)Google Scholar
- 13.Ying, Y., Wang, H., Xu, J.: An automatic system for multi-view face detection and pose estimation. In: Proceedings of the 11th International Conference on Control Automation Robotics and Vision, pp. 1101–1108 (2010)Google Scholar