3D Object Detection and Recognition for Robotic Grasping Based on RGB-D Images and Global Features

[1] Abadi M., et al., Tensorflow: Large-scale machine learning on heterogeneous systems, Software available from tensorflow. org, 2015.Search in Google Scholar

[2] Aldoma A. et al., CAD-model recognition and 6DOF pose estimation using 3D cues, IEEE International Conference on Computer Vision Workshops, 6-13 November 2011.10.1109/ICCVW.2011.6130296Search in Google Scholar

[3] Aldoma A., et al., A global hypotheses verification method for 3D object recognition, Computer Vision–ECCV, 511–524, 2012.10.1007/978-3-642-33712-3_37Search in Google Scholar

[4] Aldoma A., et al., Multimodal cue integration through Hypotheses Verification for RGB-D object recognition and 6DoF pose estimation, IEEE International Conference on Robotics and Automation (ICRA), 2104–2111, 2013.10.1109/ICRA.2013.6630859Search in Google Scholar

[5] Aldoma A., et al., OUR-CVFH – Oriented, Unique and Repeatable Clustered Viewpoint Feature Histogram for Object Recognition and 6DOF Pose Estimation, Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol. 7476, 113–122, 2012.Search in Google Scholar

[6] Aldoma A., et al., Three-Dimensional Object Recognition and 6 DoF Pose Estimation, IEEE Robotics & Automation Magazine, 80–91, September 2012.10.1109/MRA.2012.2206675Search in Google Scholar

[7] Aldoma A., Fäulhammer T., Vincze M., Automation of “Ground Truth” Annotation for Multi-View RGB-D Object Instance Recognition Datasets, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5016–5023, 2014.10.1109/IROS.2014.6943275Search in Google Scholar

[8] Aldoma A., Rusu R. B., Vincze M., 0-Order Affordances through CAD-Model Recognition and 6DOF Pose Estimation, Active Semantic Perception and Object Search in the Real World Workshop, IROS, 2011.10.1109/ICCVW.2011.6130296Search in Google Scholar

[9] Alexandre L. A., 3d object recognition using convolutional neural networks with transfer learning between input channels, 13th International Conference on Intelligent Autonomous Systems, 2014.10.1007/978-3-319-08338-4_64Search in Google Scholar

[10] Bayramoglu N., Alatan A., Shape index SIFT: Range image recognition using local features, 20th International Conference on Pattern Recognition, 352–355, 2010.10.1109/ICPR.2010.95Search in Google Scholar

[11] Bergstra J., et al., Theano: Deep learning on gpus with python, NIPS Big Learning Workshop, Granada, Spain, 2011.Search in Google Scholar

[12] Besl P., McKay N., A Method for Registration of 3-D Shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, 1992.10.1109/34.121791Search in Google Scholar

[13] Bo L., Ren X., Fox D., Unsupervised feature learning for rgb-d based object recognition. Experimental Robotics, 387–402. Springer, 2013.10.1007/978-3-319-00065-7_27Search in Google Scholar

[14] Chang A. X., et al., Shapenet: An informationrich 3d model repository, arXiv preprint arXiv:1512.03012, 2015.Search in Google Scholar

[15] Collobert R., Kavukcuoglu K., Farabet C., Torch7: A matlab-like environment for machine learning, in BigLearn, NIPS Workshop, no. EPFL-CONF-192376, 2011.Search in Google Scholar

[16] Eigen D., Fergus R., Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture, International Conference on Computer Vision, 2650–2658, 2015.10.1109/ICCV.2015.304Search in Google Scholar

[17] Gupta S., et al., Aligning 3D models to RGB-D images of cluttered scenes, Inte. Conference on Computer Vision and Pattern Recognition, 4731–4740, 2015.10.1109/CVPR.2015.7299105Search in Google Scholar

[18] He K., et al., Spatial pyramid pooling in deep convolutional networks for visual recognition, 13th European Conference on Computer Vision, 346–361, 2014.10.1007/978-3-319-10578-9_23Search in Google Scholar

[19] Hernandez C et al., Team Delft’s Robot Winner of the Amazon Picking Challenge, ArXiv eprints. arXiv: 1610.05514 [cs.RO], 2016.Search in Google Scholar

[20] Hernandez-Vela A. et al., BoVDW: Bag-of-Visual-and-Depth-Words for gesture recognition, International Conference on Pattern Recognition (ICPR), 2012.Search in Google Scholar

[21] Hinterstoisser S, et al., Dominant orientation templates for real-time detection of texture-less objects, International Conference on Computer Vision and Pattern Recognition (CVPR), 2010.10.1109/CVPR.2010.5539908Search in Google Scholar

[22] Jia Y., et al., Caffe: Convolutional architecture for fast feature embedding, arXiv preprint arXiv:1408.5093, 2014.10.1145/2647868.2654889Search in Google Scholar

[23] Jonschkowski R. et al., Probabilistic multi-class segmentation for the Amazon Picking Challenge, International Conference on Intelligent Robots and Systems (IROS), 201610.1109/IROS.2016.7758087Search in Google Scholar

[24] Kołomyjec K., Czajewski W., Identification and localization of objects in RGD-D images for the purpose of manipulation, in Polish, Prace Naukowe Politechniki Warszawskiej. Elektronika, 195, 2, 377-386, 2016.Search in Google Scholar

[25] Kurban R., Skuka F., Bozpolat H., Plane Segmentation of Kinect Point Clouds using RANSAC, 7th International Conference on Information Technology, ICIT, Amman, Jordan, 2015, 545–551.10.15849/icit.2015.0098Search in Google Scholar

[26] Lai K., et al., A Large-scale Hierarchical Multi-view RGB-D Object Dataset, IEEE International Conference on Robotics and Automation (ICRA), May 2011.10.1109/ICRA.2011.5980382Search in Google Scholar

[27] Lai K., et al., Sparse distance learning for object recognition combining rgb and depth information. IEEE International Conference on Robotics and Automation, (ICRA) May 2011.10.1109/ICRA.2011.5980377Search in Google Scholar

[28] Laptev D. et al., Ti-pooling: Transformation-invariant pooling for feature learning in convolutional neural networks, International Conference on Computer Vision and Pattern Recognition, 289–297, 2016.10.1109/CVPR.2016.38Search in Google Scholar

[29] Łępicka M., Kornuta T., Stefańczyk M., Utilization of colour in ICP-based point cloud registration, 9th International Conference on Computer Recognition Systems, 821–830, 2015, 2016.10.1007/978-3-319-26227-7_77Search in Google Scholar

[30] Martínez L., Loncomilla P., Ruiz-del-Solar J., Object recognition for manipulation tasks in real domestic settings: A comparative study, 18th RoboCup International Symposium, Lecture Notes in Computer Science. Springer, Joao Pessoa, Brazil, 2014.Search in Google Scholar

[31] Maturana D., Scherer S., Voxnet: A 3d convolutional neural network for real-time object recognition, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015.10.1109/IROS.2015.7353481Search in Google Scholar

[32] Muja M., Lowe D.G., Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration, International Conference on Computer Vision Theory and Applications (VISAPP), 2009.Search in Google Scholar

[33] Nafouki K., Object recognition and pose estimation from an RGB-D image, Technical report, Technical University of Munich, 2016.Search in Google Scholar

[34] Narayanan V., Likhachev M., PERCH: Perception via Search for Multi-Object Recognition and Localization, IEEE International Conference on Robotics and Automation (ICRA), 2016.10.1109/ICRA.2016.7487711Search in Google Scholar

[35] Papazov C., Burschka D., An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes, Asian Conference on Computer Vision, Part I, 135–148, 2010.10.1007/978-3-642-19315-6_11Search in Google Scholar

[36] Porzi Z. et al, Learning Depth-Aware Deep Representations for Robotic Perception, IEEE Robotics and Automation Letters, Volume 2, Issue 2, April 2017.10.1109/LRA.2016.2637444Search in Google Scholar

[37] Prankl J., et al., RGB-D Object Modelling for Object Recognition and Tracking, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015.10.1109/IROS.2015.7353360Search in Google Scholar

[38] Rusu R.B., Cousins R., 3D is here: Point Cloud Library (PCL), IEEE International Conference on Robotics and Automation (ICRA), Shanghai, 9–13 May 2011.10.1109/ICRA.2011.5980567Search in Google Scholar

[39] Rusu R.B., et al. Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram, 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, 10/2010, 2155–2162.10.1109/IROS.2010.5651280Search in Google Scholar

[40] Rusu, R.B., Blodow N., Beetz M., Fast point feature histograms (fpfh) for 3d registration, IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, May 12-17 2009.10.1109/ROBOT.2009.5152473Search in Google Scholar

[41] Socher R., et al., Convolutional-recursive deep learning for 3d object classification. Advances in Neural Information Processing Systems, 665-673, 2012.Search in Google Scholar

[42] Song S., Xiao J., Deep sliding shapes for amodal 3d object detection in rgb-d images, 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.10.1109/CVPR.2016.94Search in Google Scholar

[43] Tang J., et al., A textured object recognition pipeline for color and depth image data, IEEE International Conference on Robotics and Automation, 3467–3474, 2012.Search in Google Scholar

[44] Tombari F., Salti S., Di Stefano L., Unique signatures of Histograms for local surface description, European Conference on Computer Vision (ECCV), 2010.10.1007/978-3-642-15558-1_26Search in Google Scholar

[45] Wang A., et al., MMSS: Multi-modal sharable and specific feature learning for RGB-D object recognition, International Conference on Computer Vision, 1125–1133, 2015.10.1109/ICCV.2015.134Search in Google Scholar

[46] Wu Z., et al., 3dshapenets: A deep representation for volumetric shapes, IEEE Conference on Computer Vision and Pattern Recognition, 1912–1920, 2015.Search in Google Scholar

[47] Xie Z., et al., Multimodal blending for high-accuracy instance recognition, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2214–2221, 2013.Search in Google Scholar

[48] Zang Z., Iterative point matching for registration of free-form curves and surfaces, International Journal of Computer Vision, Volume 13, Issue 2, 119–152, 1994.10.1007/BF01427149Search in Google Scholar