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

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.

[1]  Antony R Mileham,et al.  Rapid and Accurate Data Integration Method for Reverse Engineering Applications , 2007 .

[2]  Chia-Hsiang Menq,et al.  Multiple-sensor integration for rapid and high-precision coordinate metrology , 2000 .

[3]  Youfu Li,et al.  Method for determining the probing points for efficient measurement and reconstruction of freeform surfaces , 2003 .

[4]  Jean-Pierre Kruth,et al.  A performance evaluation test for laser line scanners on CMMs , 2007 .

[5]  Anath Fischer,et al.  Multi-sensor Multi-resolution Data Fusion Modeling , 2014 .

[6]  Jean-Pierre Kruth,et al.  Automated dimensional inspection planning using the combination of laser scanner and tactile probe , 2012 .

[7]  Peihua Gu,et al.  Free-form surface inspection techniques state of the art review , 2004, Comput. Aided Des..

[8]  V. H. Chan,et al.  A multi-sensor approach to automating co-ordinate measuring machine-based reverse engineering , 2001 .

[9]  Colin Bradley,et al.  A Complementary Sensor Approach to Reverse Engineering , 2001 .

[10]  Alex Lallement,et al.  Multi-sensor data fusion for realistic and accurate 3d reconstruction , 2014, 2014 5th European Workshop on Visual Information Processing (EUVIP).

[11]  Hsi-Yung Feng,et al.  Error Compensation for Three-Dimensional Line Laser Scanning Data , 2001 .

[12]  G. Sansoni,et al.  Combination of a Vision System and a Coordinate Measuring Machine for the Reverse Engineering of Freeform Surfaces , 2001 .

[13]  Eduardo Cuesta,et al.  Analysis of laser scanning and strategies for dimensional and geometrical control , 2010 .

[14]  Adam Wozniak,et al.  Proximity weighted correction of high density high uncertainty (HDHU) point cloud using low density low uncertainty (LDLU) reference point coordinates , 2015 .

[15]  Liang-Chia Chen,et al.  A vision-aided reverse engineering approach to reconstructing free-form surfaces , 1997 .

[16]  Antony R Mileham,et al.  A New Data Fusion Method for Scanned Models , 2006, J. Comput. Inf. Sci. Eng..

[17]  Hsi-Yung Feng,et al.  Analysis of digitizing errors of a laser scanning system , 2001 .

[18]  Massimo Pacella,et al.  Point set augmentation through fitting for enhanced ICP registration of point clouds in multisensor coordinate metrology , 2013 .

[19]  Han Ding,et al.  Measurement Error Compensation Using Data Fusion Technique for Laser Scanner on AACMMs , 2010, ICIRA.

[20]  Xiangqian Jiang,et al.  Multisensor data fusion in dimensional metrology , 2009 .

[21]  Bartosz Gapiński,et al.  Comparison of Different Method of Measurement Geometry using CMM, Optical Scanner and Computed Tomography 3D , 2014 .