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.
Highlights
Automated three-dimensional (3D) object digitization, known under various terminologies such as 3D scanning, 3D digitizers or reconstruction, has been widely applied in many applications, such as 3D printing, reverse engineering, rapid prototyping and medical prosthetics.According to the sensing principle being employed, the current solutions can be generally classified into two main categories, namely hand-guided and automated scanning techniques
The six-axis robot arm integrated with 3D imaging scanners has recently emerged as a technical developing trend for 3D surface scanning for objects with arbitrary or complex geometry [5,6,7]
After the best matching is defined, the correspondence feature vectors FVP and FVQ can be determined. Based on these feature vectors, the initial transformation matrix T initial between two series of scans can be estimated by aligning the frame {CQ, vQ1, vQ2, vQ3 } that represents the series of scans 1 to the frame {CP, vQbest1, vQbest2, vQbest3 } that represents the series of scans 2
Summary
Automated three-dimensional (3D) object digitization, known under various terminologies such as 3D scanning, 3D digitizers or reconstruction, has been widely applied in many applications, such as 3D printing, reverse engineering, rapid prototyping and medical prosthetics. The six-axis robot arm integrated with 3D imaging scanners has recently emerged as a technical developing trend for 3D surface scanning for objects with arbitrary or complex geometry [5,6,7]. Both Callieri [5] and Larsson [6] presented a system for. 2 of3D surface scanning for objects with arbitrary or complex geometry [5,6,7] Both Callieri [5] and Larsson [6] presented a system for automated 3D modeling consisting of a 3D laser scanner, an industrial automated.
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