Research on splicing method of point cloud with insufficient features based on spatial reference

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Suppose the current three-dimensional point cloud splicing method is well applicable to point clouds with insufficient features, it will benefit the noncontact surface quality detection and reverse design of objects with insufficient surface features. However, unfortunately, because the point cloud features are not rich, the current method is not applicable. We propose a point cloud splicing method based on spatial reference objects to solve the problem. Also, we describe the data processing method in detail. We first preprocess the point cloud and segment the spherical reference point cloud. Then, fit the spherical center coordinates of the spherical point cloud. Next, we calculate the relative spatial relationship between the target point cloud and the test point cloud and achieve coarse registration. Finally, we use the iterative closest point algorithm to complete the point cloud pair’s precise registration. From the experimental results, we can find that the proposed method has a splicing root mean square error of <0.03 mm for point clouds with insufficient features. When the point clouds’ initial relative spatial positions are significantly different, the average distance between the registered point clouds is <0.04 mm. In addition, experiments have proved that the arrangement of reference objects, the point cloud’s initial relative position, and the cumulative error have little effect on point cloud splicing accuracy. The proposed method does not rely on point cloud features and can achieve high-precision splicing of point clouds with insufficient features.