Pose Estimation with Mismatching Region Detection in Robot Bin Picking

3D object detection and pose estimation based on 3D sensor have been widely studied for its applications in robotics. In this paper, we propose a new clustering strategy in Point Pair Feature (PPF) based 3D object detection and pose estimation framework to further improve the pose hypothesis result. Our main contribution is using Density Based Spatial Clustering of Applications with Noise (DBSCAN) and Principle Component Analysis (PCA) in PPF method. It was recently shown that point pair feature combined with a voting framework was able to obtain a fast and robust pose estimation result in heavily cluttered scenes with occlusions. However, this method may fail in the mismatching region caused by false features or features with insufficient information. Our experimental results show that the proposed method can detect mismatching region and false pose hypotheses in PPF method, which improves the performance in robot bin picking application.

[1]  Ming-Yu Liu,et al.  Voting-based pose estimation for robotic assembly using a 3D sensor , 2012, 2012 IEEE International Conference on Robotics and Automation.

[2]  Oliver Brock,et al.  Analysis and Observations From the First Amazon Picking Challenge , 2016, IEEE Transactions on Automation Science and Engineering.

[3]  Jianxiong Xiao,et al.  Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Luc Van Gool,et al.  Hough Transform and 3D SURF for Robust Three Dimensional Classification , 2010, ECCV.

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

[6]  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.

[7]  Darius Burschka,et al.  An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes , 2010, ACCV.

[8]  Slobodan Ilic,et al.  Point Pair Features Based Object Detection and Pose Estimation Revisited , 2015, 2015 International Conference on 3D Vision.

[9]  Toon Goedemé,et al.  Point Pair Feature Based Object Detection for Random Bin Picking , 2016, 2016 13th Conference on Computer and Robot Vision (CRV).

[10]  Sebastian Scherer,et al.  VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Vincent Lepetit,et al.  Going Further with Point Pair Features , 2016, ECCV.

[12]  Vincent Lepetit,et al.  Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes , 2012, ACCV.

[13]  Jiaolong Yang,et al.  Go-ICP: Solving 3D Registration Efficiently and Globally Optimally , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Tae-Kyun Kim,et al.  Latent-Class Hough Forests for 6 DoF Object Pose Estimation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  A. Rama Mohan Reddy,et al.  A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method , 2016, Pattern Recognit..

[16]  Tae-Kyun Kim,et al.  A learning-based variable size part extraction architecture for 6D object pose recovery in depth images , 2017, Image Vis. Comput..

[17]  Kuan-Ting Yu,et al.  Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

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

[19]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[20]  Vincent Lepetit,et al.  Gradient Response Maps for Real-Time Detection of Textureless Objects , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Mohammed Bennamoun,et al.  3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[23]  Henrik I. Christensen,et al.  RGB-D object pose estimation in unstructured environments , 2016, Robotics Auton. Syst..

[24]  Leonidas J. Guibas,et al.  Volumetric and Multi-view CNNs for Object Classification on 3D Data , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).