###### Abstract  
The paper is concerned with the problem of multi-view three-dimensional (3D) point cloud registration. A novel global registration method is proposed to accurately register two series of scans into an object model underlying 3D imaging digitization by using the proposed oriented bounding box (OBB) regional area-based descriptor. A robot 3D scanning strategy is nowadays employed to generate the complete set of point cloud of physical objects by using 3D robot multi-view scanning and data registration. The automated operation has to successively digitize view-dependent area-scanned point cloud from complex-shaped objects by simultaneous determination of the next best robot pose and multi-view point cloud registration. To achieve this, the OBB regional area-based descriptor is employed to determine an initial transformation matrix and is then refined employing the iterative closest point (ICP) algorithm. The key technical breakthrough can resolve the commonly encountered difficulty in accurately merging two neighboring area-scanned images when no coordinate reference exists. To verify the effectiveness of the strategy, the developed method has been verified through some experimental tests for its registration accuracy. Experimental results have preliminarily demonstrated the feasibility and applicability of the developed method. robot; 3D scanning; image registration; point cloud; reverse engineering; surface digitization +
Footnote †: journal: Applied  
0000-0001-5000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00000-00000-0  
automated 3D modeling consisting of a 3D laser scanner, an industrial six-axis robot and a turntable. The advantage of this system is that it automatically achieves the shape of the object from initial scanning and thus, if possible, will scan the object using an orientation of the scanner which gives the most accurate result. Meanwhile, an autonomous 3D modeling system, consisting of an industrial robot and a laser striper for efficient surface reconstruction of unknown objects was proposed by [PERSON] [7]. The system iteratively searches for possible scan paths in the local surface model and selects a next-best-scan (NBS) in its volumetric model. The data reconstruction of the robotic scanning systems reviewed above is mainly performed in two steps: data registration and data merge. First, an initial transformation matrix between two neighboring area scans can be traditionally determined by using the nominal scanning position of the robot arm or turntable as a starting reference, although it is not always accurate due to the unavoidable robot positioning errors. Thus, the matrix can serve as a reasonable initial estimate by using the Iterative Closest Point (ICP) algorithm [8] for further refinement of the transformation matrix. However, due to the lack of a global registration between successively scanned data, the existing scanning techniques still fail to robustly satisfy accurate object digitization with 100% surface coverage. Figure 1 shows two cases that are missing some parts of the object surfaces after performing automated object digitization using robot scanning. The above issue is mainly caused by the fact that 3D sensor cannot measure point data from the scanned object surface that is in contact with its supporting ground. To resolve the issue, the object can be reoriented manually or automatically by using a robot grabber. Nevertheless, the changing orientation of the object produced by these two methods will definitively lose the initial pose estimate from before and after the orientation change. To estimate the pose transformation, some existing algorithms, such as local descriptor-based coarse alignment [9; 10; 11], use local-regional surface characteristics. As a general case, to compute the initial pose estimate, the geometric distances between corresponding 3D points existing in different views are minimized. The most common correspondences are points, curves and surfaces.