2D Object Localization Based Point Pair Feature for Pose Estimation

To automate the bin picking task with robots, pose estimation is the key challenge which identifies and locates objects, thus the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel solution which combines a machine learning based 2D object localization and a non-machine learning based 3D pose estimation method to estimate the pose of randomly piled up industrial parts. Given an image of a scene, the target part is localized in 2D first and its result is then used to crop the point cloud of the target part. Using the cropped point cloud and Boundary-to-Boundary-using-Directional-Tangent-Line (B2B-DTL) point pair feature, a novel descriptor, the proposed method could estimate the pose of industrial parts whose point clouds lack key details, for example, the point cloud of ridges of a part. Our algorithm is evaluated against real scenes and its experimental results show that the proposed method is sufficiently accurate and its online computation time is short, which makes it could be used in the real factory environment.

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

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

[3]  Dieter Fox,et al.  Unsupervised Feature Learning for RGB-D Based Object Recognition , 2012, ISER.

[4]  Andrew E. Johnson,et al.  Spin-Images: A Representation for 3-D Surface Matching , 1997 .

[5]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Mingyu Li,et al.  Curve Set Feature-Based Robust and Fast Pose Estimation Algorithm , 2017, Sensors.

[7]  Nassir Navab,et al.  Model globally, match locally: Efficient and robust 3D object recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Rama Chellappa,et al.  Fast object localization and pose estimation in heavy clutter for robotic bin picking , 2012, Int. J. Robotics Res..

[9]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[10]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Jana Kosecka,et al.  3D Bounding Box Estimation Using Deep Learning and Geometry , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Florian Röhrbein,et al.  Gearing up and accelerating cross-fertilization between academic and industrial robotics research in Europe: , 2014 .

[13]  Dieter Fox,et al.  A Scalable Tree-Based Approach for Joint Object and Pose Recognition , 2011, AAAI.

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

[15]  Bolei Zhou,et al.  SegICP: Integrated deep semantic segmentation and pose estimation , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[16]  Paul J. Besl,et al.  Method for registration of 3-D shapes , 1992, Other Conferences.

[17]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Takeo Kanade,et al.  6D pose estimation of textureless shiny objects using random ferns for bin-picking , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Jun Kinugawa,et al.  Point Pair Feature-Based Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) for Robotic Bin Picking , 2018, Sensors.

[21]  Jun Li,et al.  Active Recognition and Manipulation for Mobile Robot Bin Picking , 2014, Technology Transfer Experiments from the ECHORD Project.

[22]  Jana Kosecka,et al.  Fast Single Shot Detection and Pose Estimation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

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