Model-based active viewpoint transfer for purposive perception

In vision-based manipulation tasks, it is a fundamental procedure to transfer the sensor to a target viewpoint for better observation or manipulation. However, the limitations of field of view (FOV) and obscure issues, which are common on industrial occasions, impede the application of visual perception in unstructured environment. In this paper, an online navigation planning strategy of viewpoint transfer for purposive perception is developed, then a 2D viewpoint estimation method using CNN-based spherical viewpoint node classification is presented. To satisfy the requirements of large scale annotated training samples for deep learning, a sample synthesizing method by applying virtual camera in CAD environments is also proposed. We evaluate our method on specific datasets and a UR10 robot, with the experimental results shown in high efficiency and feasibility.

[1]  Vincent Lepetit,et al.  Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes , 2011, 2011 International Conference on Computer Vision.

[2]  Darius Burschka,et al.  Textured/textureless object recognition and pose estimation using RGB-D image , 2013, Journal of Real-Time Image Processing.

[3]  Shengyong Chen,et al.  Active vision in robotic systems: A survey of recent developments , 2011, Int. J. Robotics Res..

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

[5]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[6]  Kate Saenko,et al.  Generating Large Scale Image Datasets from 3 D CAD Models , 2015 .

[7]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[8]  Sukhan Lee,et al.  3D visual perception system for bin picking in automotive sub-assembly automation , 2012, 2012 IEEE International Conference on Automation Science and Engineering (CASE).

[9]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Kai-Tai Song,et al.  CAD-based Pose Estimation Design for Random Bin Picking using a RGB-D Camera , 2017, J. Intell. Robotic Syst..

[11]  Giorgio Metta,et al.  Active object recognition on a humanoid robot , 2012, 2012 IEEE International Conference on Robotics and Automation.

[12]  Jitendra Malik,et al.  Viewpoints and keypoints , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Mathieu Aubry,et al.  Crafting a multi-task CNN for viewpoint estimation , 2016, BMVC.

[14]  Tinne Tuytelaars,et al.  A Scalable 3D HOG Model for Fast Object Detection and Viewpoint Estimation , 2014, 2014 2nd International Conference on 3D Vision.

[15]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Cordelia Schmid,et al.  Multi-view object class detection with a 3D geometric model , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  João M. F. Rodrigues,et al.  Biological Models for Active Vision: Towards a Unified Architecture , 2013, ICVS.

[18]  Silvio Savarese,et al.  Enriching object detection with 2D-3D registration and continuous viewpoint estimation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Bijoy K. Ghosh,et al.  Pose estimation using line-based dynamic vision and inertial sensors , 2003, IEEE Trans. Autom. Control..

[20]  Rong Xiong,et al.  Robust and Accurate Multiple-Camera Pose Estimation toward Robotic Applications , 2014 .

[21]  Antonio Torralba,et al.  FPM: Fine Pose Parts-Based Model with 3D CAD Models , 2014, ECCV.

[22]  Vincent Lepetit,et al.  Learning descriptors for object recognition and 3D pose estimation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).