Modreg: A Modular Framework for RGB-D Image Acquisition and 3D Object Model Registration

[1] Aldoma A., Tombari F., Di Stefano L., and Vincze M. A global hypotheses verification method for 3D object recognition. In Computer Vision (ECCV 2012), pages 511–524. Springer, 2012.10.1007/978-3-642-33712-3_37Search in Google Scholar

[2] Aldoma A., Tombari F., Prankl J., Richtsfeld A., Di Stefano L., and Vincze M. Multimodal cue integration through hypotheses verification for RGB-D object recognition and 6DOF pose estimation. In Robotics and Automation (ICRA), 2013 IEEE International Conference on, pages 2104–2111. IEEE, 2013.10.1109/ICRA.2013.6630859Search in Google Scholar

[3] Belter D., Nowicki M., Skrzypczyński P., Walas K., and Wietrzykowski J. Lightweight RGB-D SLAM System for Search and Rescue Robots. In Progress in Automation, Robotics and Measuring Techniques, pages 11–21. Springer, 2015.10.1007/978-3-319-15847-1_2Search in Google Scholar

[4] Besl P. and McKay N. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2):239 –256, 1992.10.1109/34.121791Search in Google Scholar

[5] Bradski G. and Kaehler A. Learning OpenCV: Computer vision with the OpenCV library. O’Reilly Media, 2008.Search in Google Scholar

[6] Correll N., Bekris K. E., Berenson D., Brock O., Causo A., Hauser K., Okada K., Rodriguez A., Romano J. M., and Wurman P. R. Analysis and observations from the first amazon picking challenge. IEEE Transactions on Automation Science and Engineering, 2016.Search in Google Scholar

[7] Dziergwa M., Kaczmarek P., and Kędzierski J. RGB-D Sensors in Social Robotics. Journal of Automation Mobile Robotics and Intelligent Systems, 9(1):18–27, 2015.10.14313/JAMRIS_1-2015/3Search in Google Scholar

[8] Figat J., Kornuta T., and Kasprzak W. Performance evaluation of binary descriptors of local features. In Chmielewski L., Kozera R., Shin B.-S., and Wojciechowski K., editors, Proceedings of the International Conference on Computer Vision and Graphics, volume 8671 of Lecture Notes in Computer Science, pages 187–194. Springer Berlin / Heidelberg, 2014.10.1007/978-3-319-11331-9_23Search in Google Scholar

[9] Firman M. Rgbd datasets: Past, present and future. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 19–31, 2016.10.1109/CVPRW.2016.88Search in Google Scholar

[10] Großmann B., Siam M., and Krüger V. Comparative evaluation of 3D pose estimation of industrial objects in RGB pointclouds. In Computer Vision Systems, pages 329–342. Springer, 2015.10.1007/978-3-319-20904-3_30Search in Google Scholar

[11] Hirschmuller H. Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell., pages 328–341, II 2008.10.1109/TPAMI.2007.116618084062Search in Google Scholar

[12] Holz D., Ichim A. E., Tombari F., Rusu R. B., and Behnke S. Registration with the Point Cloud Library: A Modular Framework for Aligning in 3-D. Robotics & Automation Magazine, IEEE, 22(4):110–124, 2015.Search in Google Scholar

[13] Konolige K. Projected texture stereo. In International Conference on Robotics and Automation (ICRA), pages 148–155. IEEE, 2010.10.1109/ROBOT.2010.5509796Search in Google Scholar

[14] Kornuta T. and Laszkowski M. Perception subsystem for object recognition and pose estimation in RGB-D images. In Szewczyk R., Zieliński C., and Kaliczyńska M., editors, Recent Advances in Automation, Robotics and Measuring Techniques, volume 440 of Advances in Intelligent Systems and Computing (AISC), pages 597–607. Springer, 2016.10.1007/978-3-319-29357-8_52Search in Google Scholar

[15] Kornuta T. and Stefańczyk M. Acquisition of RGB-D images: sensors (in Polish). Pomiary – Automatyka – Robotyka PAR, 18(2):92–99, 2014.Search in Google Scholar

[16] Kornuta T. and Stefańczyk M. Comparison of methods of aquisition of RGB-D images for the purpose of registration of three-dimensional models of objects (in Polish). In XIV Krajowa Konferencja Robotyki – Postepy robotyki, volume 2, pages 357–366, 2016.Search in Google Scholar

[17] Kornuta T. and Stefańczyk M. Utilization of textured stereovision for registration of 3D models of objects. In 21th IEEE International Conference on Methods and Models in Automation and Robotics, MMAR’2016, pages 1088–10093. IEEE, 2016.10.1109/MMAR.2016.7575289Search in Google Scholar

[18] Lai K., Bo L., Ren X., and Fox D. A large-scale hierarchical multi-view RGB-D object dataset. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1817–1824. IEEE, 2011.10.1109/ICRA.2011.5980382Search in Google Scholar

[19] Lenz I., Knepper R., and Saxena A. DeepMPC: Learning deep latent features for model predictive control. In Proceedings of Robotics: Science and Systems, Rome, Italy, July 2015.10.15607/RSS.2015.XI.012Search in Google Scholar

[20] Lowe D. Object recognition from local scale-invariant features. In Computer Vision, The Proceedings of the Seventh IEEE International Conference on, volume 2, pages 1150–1157. Ieee, 1999.10.1109/ICCV.1999.790410Search in Google Scholar

[21] Lu F. and Milios E. Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4(4):333–349, 1997.10.1023/A:1008854305733Search in Google Scholar

[22] Łępicka M., Kornuta T., and Stefańczyk M. Utilization of colour in ICP-based point cloud registration. In Proceedings of the 9th International Conference on Computer Recognition Systems (CORES 2015), volume 403 of Advances in Intelligent Systems and Computing, pages 821–830. Springer, 2016.10.1007/978-3-319-26227-7_77Search in Google Scholar

[23] Muja M. and Lowe D. G. Fast approximate nearest neighbors with automatic algorithm configuration. In VISAPP (1), pages 331–340, 2009.Search in Google Scholar

[24] Newcombe A. J., Richard A and Davison, Izadi S., Kohli P., Hilliges O., Shotton J., Molyneaux D., Hodges S., Kim D., and Fitzgibbon A. KinectFusion: Real-time dense surface mapping and tracking. In Mixed and augmented reality (ISMAR), 2011 10th IEEE international symposium on, pages 127–136. IEEE, 2011.10.1109/ISMAR.2011.6092378Search in Google Scholar

[25] Peng X., Sun B., Ali K., and Saenko K. Learning deep object detectors from 3d models. In Proceedings of the IEEE International Conference on Computer Vision, pages 1278–1286, 2015.10.1109/ICCV.2015.151Search in Google Scholar

[26] Pomerleau F., Colas F., and Siegwart R. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends in Robotics (FnTROB), 4(1):1–104, 2015.10.1561/2300000035Search in Google Scholar

[27] Ramey A., González-Pacheco V., and Salichs M. A. Integration of a low-cost RGB-D sensor in a social robot for gesture recognition. In Proceedings of the 6th international conference on Human-robot interaction, pages 229–230. ACM, 2011.10.1145/1957656.1957745Search in Google Scholar

[28] Ren X., Fox D., and Konolige K. Change Their Perception: RGB-D for 3-D Modeling and Recognition. Robotics & Automation Magazine, IEEE, 20(4):49–59, 2013.Search in Google Scholar

[29] Rusu R., Blodow N., and Beetz M. Fast point feature histograms (FPFH) for 3D registration. In Robotics and Automation, 2009. ICRA’09. IEEE International Conference on, pages 3212–3217. IEEE, 2009.10.1109/ROBOT.2009.5152473Search in Google Scholar

[30] Rusu R. B. and Cousins S. 3D is here: Point Cloud Library (PCL). In International Conference on Robotics and Automation, Shanghai, China, 2011 2011.10.1109/ICRA.2011.5980567Search in Google Scholar

[31] Seredyński D. and Szynkiewicz W. Fast grasp learning for novel objects. In Recent Advances in Automation, Robotics and Measuring Techniques, volume 440 of Advances in Intelligent Systems and Computing (AISC), pages 681–692. Springer, 2016.10.1007/978-3-319-29357-8_59Search in Google Scholar

[32] Sprickerhof J., Nüchter A., Lingemann K., and Hertzberg J. A heuristic loop closing technique for large-scale 6D SLAM. Automatika: Journal for Control, Measurement, Electronics, Computing & Communications, 52(3), 2011.10.1080/00051144.2011.11828420Search in Google Scholar

[33] Stefańczyk M. and Kornuta T. Acquisition of RGB-D images: methods (in Polish). Pomiary – Automatyka – Robotyka PAR, 18(1):82–90, 2014.Search in Google Scholar

[34] Stefańczyk M. and Kornuta T. Handling of asynchronous data flow in robot perception subsystems. In Simulation, Modeling, and Programming for Autonomous Robots, volume 8810 of Lecture Notes in Computer Science, pages 509–520. Springer, 2014.10.1007/978-3-319-11900-7_43Search in Google Scholar

[35] Stefańczyk M., Laszkowski M., and Kornuta T. WUT Visual Perception Dataset – a dataset for registration and recognition of objects. In Challenges in Automation, Robotics and Measurement Techniques, volume 440 of Advances in Intelligent Systems and Computing (AISC), pages 635–645. Springer, 2016.10.1007/978-3-319-29357-8_55Search in Google Scholar

[36] Sturm J., Engelhard N., Endres F., Burgard W., and Cremers D. A benchmark for the evaluation of RGB-D SLAM systems. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pages 573–580. IEEE, 2012.10.1109/IROS.2012.6385773Search in Google Scholar

[37] Tang J., Miller S., Singh A., and Abbeel P. A textured object recognition pipeline for color and depth image data. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 3467–3474. IEEE, 2012.Search in Google Scholar

[38] Thrun S., Burgard W., and Fox D. A probabilistic approach to concurrent mapping and localization for mobile robots. Autonomous Robots, 5(3-4):253–271, 1998.10.1023/A:1008806205438Search in Google Scholar

[39] Tombari F. and Di Stefano L. Object recognition in 3D scenes with occlusions and clutter by hough voting. In Image and Video Technology (PSIVT), 2010 Fourth Pacific-Rim Symposium on, pages 349–355. IEEE, 2010.10.1109/PSIVT.2010.65Search in Google Scholar

[40] Tombari F., Salti S., and Di Stefano L. Unique signatures of histograms for local surface description. In Computer Vision–ECCV 2010, pages 356–369. Springer, 2010.10.1007/978-3-642-15558-1_26Search in Google Scholar