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Abstract

This paper addresses the problem of automated registration of multi-view point clouds generated by a 3D scanner using sphere targets. First, sphere targets are detected from each point cloud. The centroids of the detected targets in each point cloud are then used for rough registration. Congruent triangles are computed from the centroids for the correspondence among them, with which a rigid body transformation is obtained to bring the two point clouds together as closely as possible. After the initial registration, the two point clouds are further registered by refining the position and orientation of the point clouds using the underlying geometric shapes of the targets. These registration steps are integrated into one system that allows two input point clouds automatically registered with no user intervention. Real examples are used to demonstrate the performance of the point cloud registration.

6. Conclusion

In this paper, the problem of registration of point clouds is addressed, and a new method for registering point clouds using sphere targets is proposed. The procedure consists of filtering and registration. The filtering step processes a point cloud, eliminates unnecessary points and detects sphere targets. The registration step registers two input point clouds using the detected targets through the initial and fine registration. The initial registration uses the centroids of the targets and brings the point clouds together as closely as possible. The fine registration refines the registration to improve the accuracy of registration. In particular, an innovative method for fine registration is proposed utilizing the underlying component geometry during registration. Theoretically, it would be possible for a CAD model to be used instead of sphere targets for registration. However, to use it as a replacement for sphere targets, the point clouds should be processed to detect the CAD model from an input point cloud. Once the model is detected, it could be used as features for registration. A CAD model may consist of various parts. Robustly detecting a CAD model from a point cloud is, however, a very difficult process in general because the model may contain complex shapes that are difficult to detect from a point cloud. In contrast, sphere targets are easy to detect robustly because of their well-defined geometric properties. Therefore, using sphere targets in the registration process, instead of using a CAD model, is preferable.