Towards precise real-time 3D difference detection for industrial applications

3D difference detection is the task to verify whether the 3D geometry of a real object exactly corresponds to a 3D model of this object. We present an approach for 3D difference detection with a hand-held depth camera. In contrast to previous approaches, with the presented approach geometric differences can be detected in real-time and from arbitrary viewpoints. The 3D difference detection accuracy is improved by two approaches: first, the precision of the depth camera's pose estimation is improved by coupling the depth camera with a high precision industrial measurement arm. Second, the influence of the depth measurement noise is reduced by integrating a 3D surface reconstruction algorithm. The effects of both enhancements are quantified by a ground-truth based quantitative evaluation, both for a time-of-flight (SwissRanger 4000) and a structured light depth camera (Kinect). With the proposed enhancements, differences of few millimeters can be detected from 1m measurement distance.

[1]  Roger Y. Tsai,et al.  A new technique for fully autonomous and efficient 3D robotics hand/eye calibration , 1988, IEEE Trans. Robotics Autom..

[2]  Lihui Wang,et al.  Review: Advances in 3D data acquisition and processing for industrial applications , 2010 .

[3]  Gerd Hirzinger,et al.  Extrinsic and depth calibration of ToF-cameras , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Svenja Kahn,et al.  Fusing Real-Time Depth Imaging with High Precision Pose Estimation by a Measurement Arm , 2012, 2012 International Conference on Cyberworlds.

[5]  Carlos H. Caldas,et al.  Integrating data from 3D CAD and 3D cameras for Real-Time Modeling , 2006 .

[6]  Slobodan Ilic,et al.  RGB-D camera-based parallel tracking and meshing , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[7]  Didier Stricker,et al.  3D shape scanning with a Kinect , 2011, SIGGRAPH '11.

[8]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

[9]  Didier Stricker,et al.  Real-time vision-based tracking and reconstruction , 2007, Journal of Real-Time Image Processing.

[10]  Reinhard Koch,et al.  Time-of-Flight Sensors in Computer Graphics , 2009, Eurographics.

[11]  Minh N. Do,et al.  A revisit to MRF-based depth map super-resolution and enhancement , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  Didier Stricker,et al.  3D discrepancy check via Augmented Reality , 2010, 2010 IEEE International Symposium on Mixed and Augmented Reality.

[13]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[14]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[15]  Gerd Hirzinger,et al.  Optimal Hand-Eye Calibration , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Nina Amenta,et al.  Laser Scanner Super-resolution , 2006, PBG@SIGGRAPH.

[18]  Kamel Hamrouni,et al.  Non-parametric depth calibration of a TOF camera , 2012, 2012 19th IEEE International Conference on Image Processing.

[19]  Olivier D. Faugeras,et al.  Shape From Shading , 2006, Handbook of Mathematical Models in Computer Vision.

[20]  Sebastian Thrun,et al.  An Application of Markov Random Fields to Range Sensing , 2005, NIPS.

[21]  Giovanna Sansoni,et al.  State-of-The-Art and Applications of 3D Imaging Sensors in Industry, Cultural Heritage, Medicine, and Criminal Investigation , 2009, Sensors.

[22]  Harald Wuest,et al.  Efficient line and patch feature characterization and management for real-time camera tracking , 2008 .

[23]  Alfred Mertins,et al.  Super resolution of time-of-flight depth images under consideration of spatially varying noise variance , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[24]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[26]  O. Loffeld,et al.  Comparison of Depth Super-Resolution Methods for 2 D / 3 D Images , 2011 .

[27]  Frédéric Bosché,et al.  Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction , 2010, Adv. Eng. Informatics.

[28]  Svenja Kahn,et al.  Reducing the gap between Augmented Reality and 3D modeling with real-time depth imaging , 2013, Virtual Reality.

[29]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[30]  Nassir Navab,et al.  Photo-based Industrial Augmented Reality application using a single keyframe registration procedure , 2009, 2009 8th IEEE International Symposium on Mixed and Augmented Reality.

[31]  Aaron Paolini,et al.  Development of a GPU-accelerated super resolution solver , 2009, Defense + Commercial Sensing.

[32]  Sebastian Thrun,et al.  LidarBoost: Depth superresolution for ToF 3D shape scanning , 2009, CVPR.

[33]  Derek D. Lichti,et al.  PERFORMANCE ANALYSIS OF A LOW-COST TRIANGULATION-BASED 3D CAMERA: MICROSOFT KINECT SYSTEM , 2012 .

[34]  Reinhard Koch,et al.  Time-of-Flight sensor calibration for accurate range sensing , 2010, Comput. Vis. Image Underst..

[35]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Sebastian Thrun,et al.  3D shape scanning with a time-of-flight camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[37]  Bernd Jähne,et al.  A theoretical and experimental investigation of the systematic errors and statistical uncertainties of Time-Of-Flight-cameras , 2008, Int. J. Intell. Syst. Technol. Appl..

[38]  Nassir Navab,et al.  An Industrial Augmented Reality Solution For Discrepancy Check , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[39]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[40]  Frédéric Bosché,et al.  Automated Recognition of 3D CAD Model Objects in Dense Laser Range Point Clouds , 2008 .

[41]  Nassir Navab,et al.  User Interfaces Navigation Tools for Viewing Augmented CAD Models , 2009 .

[42]  Baoxin Li,et al.  Fast GPU implementation of large scale dictionary and sparse representation based vision problems , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[43]  Thierry Oggier,et al.  Miniature 3D TOF Camera for Real-Time Imaging , 2006, PIT.

[44]  Pietro Zanuttigh,et al.  A Novel Interpolation Scheme for Range Data with Side Information , 2009, 2009 Conference for Visual Media Production.

[45]  Didier Stricker,et al.  Identifying differences between CAD and physical mock-ups using AR , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[46]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[47]  Pedro Arias,et al.  Metrological evaluation of Microsoft Kinect and Asus Xtion sensors , 2013 .