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Point set registration is a key method in computer vision and pattern recognition. In this paper, the correntropy and bi-directional distance are introduced into registration framework and a new robust registration model for RGB-D data is proposed. Firstly, as registering point sets with smooth structure, such as surface or plane, is easy failed, the color and position information is fused to establish more precise correspondence between two RGB-D data sets. Secondly, to reduce the influence of noises and eliminate outliers, the registration model based on the maximum correntropy criterion is established. Thirdly, the bi-directional distance measurement is introduced into the registration framework to avoid the model being trapped into local extremum. In addition, to solve this new registration problem, a new iterative closest point (ICP) algorithm is proposed, which converges to the local optimal solution by iterations. In the experiments, the proposed algorithm achieves more robustness and precise registration results than other algorithms.

With the development of RGBD sensors, the high-quality color point cloud can be obtained expediently. In this paper, we propose a novel registration method for 3D color point clouds from different views, which is a critical issue in many applications. Different from traditional feature-based methods, we design a hybrid feature representation with color moments of the point, which could be applied naturally for any color point cloud. And these features are extracted from point clouds based on the supervoxel segmentation. By jointly conducting these features for similarity measure, a weight parameter is dynamically adapted between the color and the spatial information. The registration algorithm is under a classic iterative framework for building the correspondence and estimating transformation parameters. In addition, we provide a mutual correspondence matching condition with hybrid features to build some more robust relationships for estimating transformation parameters. Experimental results demonstrate that our method can effectively reduce the number of point data for registration and achieve good matching results even in a poor initial condition. INDEX TERMSColor point cloud registration, hybrid feature, mutual correspondence matching.

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