{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:49:35Z","timestamp":1760402975461,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,2]],"date-time":"2022-01-02T00:00:00Z","timestamp":1641081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009157","name":"National Institute on Disability, Independent Living, and Rehabilitation Research","doi-asserted-by":"publisher","award":["H133G120275"],"award-info":[{"award-number":["H133G120275"]}],"id":[{"id":"10.13039\/100009157","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1409823","IIS-1527794"],"award-info":[{"award-number":["IIS-1409823","IIS-1527794"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Motivated by grasp planning applications within cluttered environments, this paper presents a novel approach to performing real-time surface segmentations of never-before-seen objects scattered across a given scene. This approach utilizes an input 2D depth map, where a first principles-based algorithm is utilized to exploit the fact that continuous surfaces are bounded by contours of high gradient. From these regions, the associated object surfaces can be isolated and further adapted for grasp planning. This paper also provides details for extracting the six-DOF pose for an isolated surface and presents the case of leveraging such a pose to execute planar grasping to achieve both force and torque closure. As a consequence of the highly parallel software implementation, the algorithm is shown to outperform prior approaches across all notable metrics and is also shown to be invariant to object rotation, scale, orientation relative to other objects, clutter, and varying degree of noise. This allows for a robust set of operations that could be applied to many areas of robotics research. The algorithm is faster than real time in the sense that it is nearly two times faster than the sensor rate of 30 fps.<\/jats:p>","DOI":"10.3390\/robotics11010007","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Faster than Real-Time Surface Pose Estimation with Application to Autonomous Robotic Grasping"],"prefix":"10.3390","volume":"11","author":[{"given":"Yannick","family":"Roberts","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, University of Central Florida (UCF), Orlando, FL 32816, USA"}]},{"given":"Amirhossein","family":"Jabalameli","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, University of Central Florida (UCF), Orlando, FL 32816, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8458-1591","authenticated-orcid":false,"given":"Aman","family":"Behal","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, NSTC at UCF, Orlando, FL 32816, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.robot.2011.07.016","article-title":"An overview of 3D object grasp synthesis algorithms","volume":"60","author":"Sahbani","year":"2012","journal-title":"Robot. Auton. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/TRO.2013.2289018","article-title":"Data-Driven Grasp Synthesis\u2014A Survey","volume":"30","author":"Bohg","year":"2014","journal-title":"IEEE Trans. Robot."},{"key":"ref_3","unstructured":"Miller, A., Knoop, S., Christensen, H., and Allen, P. (2003, January 14\u201319). Automatic Grasp Planning Using Shape Primitives. Proceedings of the Robotics and Automation, 2003. Proceedings. ICRA\u201903, IEEE International Conference, Taipei, Taiwan."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huebner, K., and Kragic, D. (2008, January 22\u201326). Selection of robot pre-grasps using box-based shape approximation. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650722"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Przybylski, M., Asfour, T., and Dillmann, R. (2011, January 25\u201330). Planning grasps for robotic hands using a novel object representation based on the medial axis transform. Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6048625"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Detry, R., Ek, C.H., Madry, M., and Kragic, D. (2013, January 6\u201310). Learning a dictionary of prototypical grasp-predicting parts from grasping experience. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630635"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kroemer, O., Ugur, E., Oztop, E., and Peters, J. (2012, January 14\u201318). A kernel-based approach to direct action perception. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MI, USA.","DOI":"10.1109\/ICRA.2012.6224957"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kopicki, M., Detry, R., Schmidt, F., Borst, C., Stolkin, R., and Wyatt, J.L. (June, January 31). Learning dexterous grasps that generalise to novel objects by combining hand and contact models. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907647"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1177\/0278364907087172","article-title":"Robotic grasping of novel objects using vision","volume":"27","author":"Saxena","year":"2008","journal-title":"Int. J. Robot. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Morrison, D., Corke, P., and Leitner, J. (2018, January 26\u201328). Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach. Proceedings of the Robotics: Science and Systems (RSS), Pittsburgh, PA, USA.","DOI":"10.15607\/RSS.2018.XIV.021"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liang, H., Ma, X., Li, S., Gorner, M., Tang, S., Fang, B., Sun, F., and Zhang, J. (2019, January 20\u201324). PointNetGPD: Detecting grasp configurations from point sets. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8794435"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mahler, J., Liang, J., Niyaz, S., Laskey, M., Doan, R., Liu, X., Ojea, J.A., and Goldberg, K. (2017, January 12\u201316). Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. Proceedings of the Robotics: Science and Systems, Cambridge, MA, USA.","DOI":"10.15607\/RSS.2017.XIII.058"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kappler, D., Bohg, J., and Schaal, S. (2015, January 26\u201330). Leveraging big data for grasp planning. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7139793"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pinto, L., and Gupta, A. (2016, January 16\u201321). Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487517"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Vahrenkamp, N., Wachter, M., and Asfour, T. (2018, January 21\u201325). Grasping of unknown objects using deep convolutional neural networks based on depth images. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8463204"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/LRA.2017.2651945","article-title":"Modeling grasp motor imagery through deep conditional generative models","volume":"2","author":"Veres","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1126\/scirobotics.aau4984","article-title":"Learning ambidextrous robot grasping policies","volume":"4","author":"Mahler","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Monta\u00f1o, A., and Su\u00e1rez, R. (2019). Dexterous Manipulation of Unknown Objects Using Virtual Contact Points. Robotics, 8.","DOI":"10.3390\/robotics8040086"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jabalameli, A., and Behal, A. (2019). From Single 2D Depth Image to Gripper 6D Pose Estimation: A Fast and Robust Algorithm for Grabbing Objects in Cluttered Scenes. Robotics, 8.","DOI":"10.3390\/robotics8030063"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nguyen, V. (1986, January 7\u201310). Constructing force-closure grasps. Proceedings of the 1986 IEEE International Conference on Robotics and Automation, San Francisco, CA, USA.","DOI":"10.1109\/ROBOT.1986.1087483"},{"key":"ref_21","unstructured":"Roberts, Y., Jabalameli, A., and Behal, A. (2021, September 12). Surface Segmentation Video Demonstration. Available online: https:\/\/youtu.be\/2KkEk_INvTM."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/0734-189X(85)90016-7","article-title":"Topological structural analysis of digitized binary images by border following","volume":"30","author":"Suzuki","year":"1985","journal-title":"Comput. Vision Graph. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sweeney, C., Izatt, G., and Tedrake, R. (2019, January 20\u201324). A Supervised Approach to Predicting Noise in Depth Images. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793820"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A Computational Approach to Edge Detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/34.56190","article-title":"Analysis of thinning algorithms using mathematical morphology","volume":"12","author":"Jang","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/11\/1\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:12:30Z","timestamp":1760364750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/11\/1\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,2]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["robotics11010007"],"URL":"https:\/\/doi.org\/10.3390\/robotics11010007","relation":{},"ISSN":["2218-6581"],"issn-type":[{"type":"electronic","value":"2218-6581"}],"subject":[],"published":{"date-parts":[[2022,1,2]]}}}