3D object recognition using spin-images for a humanoid stereoscopic vision system

This paper presents a 3D object recognition method based on spin-images for a humanoid robot having a stereoscopic vision system. Spin-images have been proposed to search CAD models database, and use 3D range informations. In this context, the use of a vision system is taken into account through a multi-resolution approach. A method for quickly computing multi-resolution and interpolating spin-images is proposed. The results on simulation and on real data are given, and show the effectiveness of this method

[1]  Szymon Rusinkiewicz,et al.  Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors , 2003, Symposium on Geometry Processing.

[2]  Fumio Kanehiro,et al.  Humanoid robot HRP-2 , 2008, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[3]  Shuangshuang Jin,et al.  A comparison of algorithms for vertex normal computation , 2005, The Visual Computer.

[4]  Michel Dhome,et al.  Real time tracking of 3D objects: an efficient and robust approach , 2002, Pattern Recognit..

[5]  Olivier Stasse,et al.  3D boundaries partial representation of objects using interval analysis , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[6]  Shuuji Kajita,et al.  OpenHRP: Open Architecture Humanoid Robotics Platform , 2004, Int. J. Robotics Res..

[7]  Michael Brady,et al.  Practical Structure and Motion from Stereo When Motion is Unconstrained , 2000, International Journal of Computer Vision.

[8]  Kenji KANEKO,et al.  Humanoid robot HRP-3 , 2004, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Jonathan M. Garibaldi,et al.  Fast, unconstrained camera motion estimation from stereo without tracking and robust statistics , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[11]  Yoshihiro Kawai,et al.  3D Object Recognition in Cluttered Environments by Segment-Based Stereo Vision , 2004, International Journal of Computer Vision.

[12]  Ales Ude,et al.  Probabilistic detection and tracking at high frame rates using affine warping , 2002, Object recognition supported by user interaction for service robots.

[13]  Rémy Prost,et al.  Wavelet-based progressive compression scheme for triangle meshes: wavemesh , 2004, IEEE Transactions on Visualization and Computer Graphics.

[14]  Jitendra Malik,et al.  Recognizing Objects in Range Data Using Regional Point Descriptors , 2004, ECCV.