The use of low density high accuracy (LDHA) data for correction of high density low accuracy (HDLA) point cloud. (June 2016)

• Record Type: Journal Article
• Authors: Rak, Michal Bartosz; Wozniak, Adam; Mayer, J.R.R.
• Is Part Of: Optics and lasers in engineering. Volume 81(2016:)
• Page Start: 140
• Page End: 150
• Publication Date: 2016-06
• Subjects: Data fusion -- Virtual markers -- Laser scanning -- Coordinate metrology -- Data processing; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2016.01.005
• Languages: English
• ISSNs: 0143-8166

Abstract

Abstract: Coordinate measuring techniques rely on computer processing of coordinate values of points gathered from physical surfaces using contact or non-contact methods. Contact measurements are characterized by low density and high accuracy. On the other hand optical methods gather high density data of the whole object in a short time but with accuracy at least one order of magnitude lower than for contact measurements. Thus the drawback of contact methods is low density of data, while for non-contact methods it is low accuracy. In this paper a method for fusion of data from two measurements of fundamentally different nature: high density low accuracy (HDLA) and low density high accuracy (LDHA) is presented to overcome the limitations of both measuring methods. In the proposed method the concept of virtual markers is used to find a representation of pairs of corresponding characteristic points in both sets of data. In each pair the coordinates of the point from contact measurements is treated as a reference for the corresponding point from non-contact measurement. Transformation enabling displacement of characteristic points from optical measurement to their match from contact measurements is determined and applied to the whole point cloud. The efficiency of the proposed algorithm was evaluated by comparison with data from a coordinate measuring machine (CMM). Three surfaces were used for this evaluation: plane, turbine blade and engine cover. For the planar surface the achieved improvement was of around 200 µm. Similar results were obtained for the turbine blade but for the engine cover the improvement was smaller. For both freeform surfaces the improvement was higher for raw data than for data after creation of mesh of triangles. Highlights: A method of merging data from two sensors is proposed. Producing a result which has the density of non-contact data but the accuracy of contact data. An algorithm propagates the difference between paired contact and non-contact points to the other non-contact data points.