International Journal of Computer Science Issues

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The paper proposed a new 3-D measurement point cloud splicing algorithm. The algorithm utilizes registration ideal in model identification technology to realize unbound and accurate splicing of 3-D data. First, sample the overlapping areas in the two 3-D point clouds which need to be spliced. Carry out pre-processing over the sampled point cloud with principal analysis method based on the statistic theory. Through extracting the feature vector that could best indicate the point cloud information, it realizes the dimension reduction for data. Then, apply improved iterate corresponding point algorithm to the sampled point cloud data which has realized pre-registration to achieve accurate registration. In the process, the set of progressive decrease of iterate condition by different levels reduced the iterate times. The utilization of new comprehensive distance measurement function effectively increases the accuracy and robustness of overall iterated convergence. Finally, apply the transformation parameter based on local sampled point cloud calculation to the entire point cloud splicing and achieve the accurate registration of multiple sampled point cloud. In the end, the actual test proved that the algorithm boasts high splicing accuracy with high overall convergence robustness, few convergence iterate times and strong anti-noise capacity.