This paper proposes an automatic model-based viewpoint planning method, which can achieve high precision and high efficiency for freeform surfaces inspection using plane structured light scanners. The surface model is...This paper proposes an automatic model-based viewpoint planning method, which can achieve high precision and high efficiency for freeform surfaces inspection using plane structured light scanners. The surface model is utilized in stereolithography format, which is widely used as an industrial standard. The proposed method consists of 4 steps： topology reconstruction, mesh refinement, scan direction determination and viewpoint generation. In the first step, the topology structure of the surface model is reconstructed according to a designed data structure, based on which a neighborhood search algorithm is developed. In the second step, big facets in the surface model are segmented into several small ones, which are suitable for viewpoint planning. In the third step, an initial scan region of a viewpoint is grouped by the neighborhood search algorithm combining with total area and normal vector restrictions. Accordingly, the scan direction is determined by the normal vectors of facets in the initial scan region. In the fourth step, the position, the orientation, and the final scan region of the viewpoint are determined by 4 scan constraints, i.e., field of view, working distance range, view angle and overlap. Experimental results verify the effectiveness and advantages of the proposed method.展开更多
文摘This paper proposes an automatic model-based viewpoint planning method, which can achieve high precision and high efficiency for freeform surfaces inspection using plane structured light scanners. The surface model is utilized in stereolithography format, which is widely used as an industrial standard. The proposed method consists of 4 steps： topology reconstruction, mesh refinement, scan direction determination and viewpoint generation. In the first step, the topology structure of the surface model is reconstructed according to a designed data structure, based on which a neighborhood search algorithm is developed. In the second step, big facets in the surface model are segmented into several small ones, which are suitable for viewpoint planning. In the third step, an initial scan region of a viewpoint is grouped by the neighborhood search algorithm combining with total area and normal vector restrictions. Accordingly, the scan direction is determined by the normal vectors of facets in the initial scan region. In the fourth step, the position, the orientation, and the final scan region of the viewpoint are determined by 4 scan constraints, i.e., field of view, working distance range, view angle and overlap. Experimental results verify the effectiveness and advantages of the proposed method.
