A 3-D point clouds scanning and registration methodology for automatic object digitization. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Chen, Liang-Chia; Hoang, Dinh-Cuong; Lin, Hsien-I; Nguyen, Thanh-Hung
• Is Part Of: Smart science. Volume 4:Issue 1(2016)
• Page Start: 1
• Page End: 7
• Publication Date: 2016-01-02
• Subjects: Robot vision -- point clouds -- object reconstruction -- 3-D image registration -- next-best-view planning
• DOI: Http://www.tandfonline.com/doi/10.1080/23080477.2016.1145459
• Languages: English
• ISSNs: 2308-0477

Abstract

Abstract: The article presents a robot 3-D scanning system for generation of 3-D point clouds of an object by using multi-view 3-D scanning and novel data registration. Our approach mainly comprises two important elements in the determination of next best probe pose and multiple-view point clouds registration. A novel technique is proposed to register 3-D object scene with overlapped or stacked condition. Under this scenario, conventional registration methods such as the iterative closest point algorithm usually fail to converge to a global minimum when a good initial estimate for image registration does not exist. Our proposed technique uses a 3-D scanner to be mounted on a six degree of freedom-articulated industrial robot. It keeps moving probe continuously in the working range against the object and autonomously varying the probe with various gestures required for complete object scanning and for achieving best 3-D sensing accuracy. The robot scanning path is determined through a proposed algorithm using information from the latest scanning data and registered result of the object. The developed method has been verified through experimental tests for its feasibility test. It confirms that the registration accuracy with one standard deviation can be controlled within 0.323 mm when the objects underlying reconstruction are in range of hundreds of millimeters.