3D Registration for Verification of Humanoid Justin's Upper Body Kinematics

Humanoid robots such as DLR's Justin are built with light-weight structures and flexible mechanical components. These generate positioning errors at the TCP (Tool-Center-Point) end-pose of the hand. The identification of these errors is essential for object manipulation and path planning. We proposed a verification routine to identify the bounds of the TCP end-pose errors by using the on-board stereo vision system. It involves estimating the pose of 3D point clouds of Justin's hand by using state-of-the-art 3D registration techniques. Partial models of the hand were generated by registering subsets of overlapping 3D point clouds. We proposed a method for the selection of overlapping point clouds of self-occluding objects (Justin's hand). It is based on a statistical analysis of the depth values. We applied an extended metaview registration method to the resulting subset of point clouds. The partial models were evaluated with detailed based surface consistency measures. The TCP end-pose errors estimated by using our method are consistent with ground-truth errors.

[1]  Ramin Zabih,et al.  A non-parametric approach to visual correspondence , 1996 .

[2]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[4]  Martial Hebert,et al.  Fully automatic registration of multiple 3D data sets , 2003, Image Vis. Comput..

[5]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Kostas Daniilidis,et al.  Fully Automatic Registration of 3D Point Clouds , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Kari Pulli,et al.  Multiview registration for large data sets , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[8]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[9]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[10]  E Dombre,et al.  Geometric calibration of robots , 2002 .

[11]  Gerhard Paar,et al.  Window Detection from Terrestrial Laser Scanner Data - A Statistical Approach , 2009, VISAPP.

[12]  Federico Tombari,et al.  A combined texture-shape descriptor for enhanced 3D feature matching , 2011, 2011 18th IEEE International Conference on Image Processing.

[13]  M. Rubin,et al.  Arithmetic and geometric solutions for average rigid-body rotation , 2010 .

[14]  H. Hirschmüller.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Stereo Processing by Semi-global Matching and Mutual Information , 2022 .

[15]  Tim Bodenmüller,et al.  Streaming surface reconstruction from real time 3D-measurements , 2009 .

[16]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

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

[18]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[19]  Alin Albu-Schäffer,et al.  The DLR lightweight robot: design and control concepts for robots in human environments , 2007, Ind. Robot.

[20]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[21]  Radu Bogdan Rusu,et al.  Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments , 2010, KI - Künstliche Intelligenz.

[22]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..