Abstract
During grasping and intelligent robotic manipulation tasks, the camera position relative to the scene changes dramatically because the robot is moving to adapt its path and correctly grasp objects. This is because the camera is mounted at the robot effector. For this reason, in this type of environment, a visual recognition system must be implemented to recognize and “automatically and autonomously” obtain the positions of objects in the scene. Furthermore, in industrial environments, all objects that are manipulated by robots are made of the same material and cannot be differentiated by features such as texture or color. In this work, first, a study and analysis of 3D recognition descriptors has been completed for application in these environments. Second, a visual recognition system designed from specific distributed client-server architecture has been proposed to be applied in the recognition process of industrial objects without these appearance features. Our system has been implemented to overcome problems of recognition when the objects can only be recognized by geometric shape and the simplicity of shapes could create ambiguity. Finally, some real tests are performed and illustrated to verify the satisfactory performance of the proposed system.
Similar content being viewed by others
References
Ferreira M, Costa P, Rocha L (2014) Stereo-based real-time 6-DoFwork tool tracking for robot programing by demonstration. Int J Adv Manuf Technol. doi:10.1007/s00170-014-6026-x
Fuchs S, Haddadin S, Keller M, Parusel S, Kolb A, Suppa M (2010) Cooperative Bin-piching with time-of-flight camera and impedance controlled DLR lightweight robot III. The 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 4862-4867.
Shmukler A, Fischer A (2009) Verification of 3D freeform parts by registration of multiscale shape descriptors. Int J Adv Manuf Technol. doi:10.1007/s00170-009-2447-3
Zheng XJ, Wang YS, Teng HF, Qu FZ (2008) Local scale-based 3D model retrieval for design reuse. Int J Adv Manuf Technol. doi:10.1007/s00170-008-1701-4
Papazov C, Haddadin S, Parusel S, Kriege K, and Burschka D (2012) Rigid 3D geometry matching for grasping of known objects in cluttered scenes. SAGE. The International Journal of Robotics Research (ijrr), 1-16, doi:10.1177/0278364911436019
Ciocarlie M, Hsiao K, Gil-Jones E, Chitta S, Rusu R, Sucan IA (2014) Towards reliable grasping and manipulation in household. In: Khatib O, Kumar V, Sukhatme G (eds) Experimental Robotics, Springer tracts in advanced robotics. Springer, Berlin, pp 241–252, Volume 79
Duncan K, Sarkar S, Alquasemi R, Dubey R (2013) Multi-scale superquadractic fitting for efficient shape and pose recovery of unknown objects. IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 4238-4243.
Sun K, Heß R, Zhihao X, Schilling K (2014) Real-time robust six degrees of freedom object pose estimation with a time-of-flight camera and a color camera. Journal of Field Robotics. doi:10.1002/rob.21519
Pfitzner C, Antal W, Hess P, May S (2014) 3D multi-sensor data fusion for object localization in industrial applications. In Proceedings of ISR/Robotik, 41st International Symposium on Robotics, 1-6, ISBN: 978-3-8007-3601-0
Adán A, Merchán P, Salamanca S (2011) 3D scene retrieval and recognition with depth gradient images. Pattern Recogn Lett 32(9):1337–1353. doi:10.1016/j.patrec.2011.03.016
Bellandi P, Docchio F, Sansoni G (2013) Roboscan: a combined 2D and 3D vision system for improved speed and flexibility in pick-and-place operation. Int J Adv Manuf Technol 69:1873–1886
Aldoma A, Marton ZC, Tombari F, Wohlkinger W, Potthast C, Zeisl B, Rusu RB, Gedikli S, Vincze M (2012) Tutorial: Point cloud library: three-dimensional object recognition and 6 dof pose estimation. IEEE Robot Autom Mag 1070(9932/12):80–91
Rusu R.B., Bradski G., Thibaux R., Hsu J. (2010) Fast 3D recognition and pose using the viewpoint feature histogram. In Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, Issue Date: 18-22 Oct. 2010, 2155 - 2162, ISBN 978-1-4244-6674-0
Salti S, Tombari F, Di-Stefano L (2014) SHOT: unique signatures of Surface and texture description. Comput Vis Image Underst 125:251–264. doi:10.1016/j.cviu.2014.04.011, ELSEVIER
Aldoma A, Tombari F, Rusu R, Vincze M (2012) OUR-CVFH – oriented, unique and repeatable clustered viewpoint feature histogram for object recognition and 6DOF pose estimation. In Pattern Recognition. Joint 34th DAGM and 36th OAGM Symposium, Graz, Austria. Proceedings; 7476; 113-122
Mateo CM, Gil P, Torres F (2014) A performance evaluation of surface normals-based descriptors for recognition of objects using CAD-models. 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Viena, Austria, 428-435.
Holzer S, Rusu RB, Dixon M, Gedikli S, Navab N (2012) Adaptive neighborhood selection for real-time surface normal estimation from organized pointcloud data using integral images IEEE/RSJ International Conference on Intelligent Robots and Systems, Portugal. doi: 10.1109/IROS.2012.6385999
Trevor A, Rusu RB, Christensen HI (2013) Efficient organized point cloud segmentation with connected components, in: Proceedings of the 3rd Workshop on Semantic Perception,
Besl PJ, McKay ND (1992) A method for registration of 3-D Shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256, IEEE Log Number 9102686
Mian A, Bennamoun M, Owens R (2006) Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans. PAMI (10) doi: 10.1109/TPAMI.2006.213
Pomares J, Gil P, Torres F (2010) Visual control of robots using range images. Sensors 10(8):7303–7322. doi:10.3390/s100807303
Sucan IA, Chitta S (2011) “MoveIt!”, [Online] Available: http://moveit.ros.org
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mateo, C.M., Gil, P. & Torres, F. Visual perception for the 3D recognition of geometric pieces in robotic manipulation. Int J Adv Manuf Technol 83, 1999–2013 (2016). https://doi.org/10.1007/s00170-015-7708-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-7708-8