{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:26:54Z","timestamp":1761744414440,"version":"build-2065373602"},"reference-count":16,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T00:00:00Z","timestamp":1479859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) descriptor for the 3D point cloud data then encodes the geometry and viewpoint, so an object can be simultaneously recognized and registered in a stable pose and the information is stored in a database. The VFH is robust to a large degree of surface noise and missing depth information so it is reliable for stereo data. However, the pose estimation for an object fails when the object is placed symmetrically to the viewpoint. To overcome this problem, this study proposes a modified viewpoint feature histogram (MVFH) descriptor that consists of two parts: a surface shape component that comprises an extended fast point feature histogram and an extended viewpoint direction component. The MVFH descriptor characterizes an object\u2019s pose and enhances the system\u2019s ability to identify objects with mirrored poses. Finally, the refined pose is further estimated using an iterative closest point when the object has been recognized and the pose roughly estimated by the MVFH descriptor and it has been registered on a database. The estimation results demonstrate that the MVFH feature descriptor allows more accurate pose estimation. The experiments also show that the proposed method can be applied in vision-guided robotic grasping systems.<\/jats:p>","DOI":"10.3390\/s16111969","type":"journal-article","created":{"date-parts":[[2016,11,23]],"date-time":"2016-11-23T11:12:14Z","timestamp":1479899534000},"page":"1969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Three-Dimensional Object Recognition and Registration for Robotic Grasping Systems Using a Modified Viewpoint Feature Histogram"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7060-1344","authenticated-orcid":false,"given":"Chin-Sheng","family":"Chen","sequence":"first","affiliation":[{"name":"Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Po-Chun","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2793-5231","authenticated-orcid":false,"given":"Chih-Ming","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taipei University of Technology, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Bradski, G., Thibaux, R., and Hsu, J. (2010, January 18\u201322). Fast 3D recognition and pose using the viewpoint feature histogram. Proceedings of the 2010 IEEE\/RSJ International Conference on the Intelligent Robots and Systems (IROS), Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5651280"},{"key":"ref_2","unstructured":"Rusu, R.B., Holzbach, A., and Beetz, M. (October, January 27). Detecting and Segmenting Objects for Mobile Manipulation. Proceedings of the IEEE Workshop on Search in 3D and Video (S3DV), Held in Conjunction with the 12th IEEE international Conference on Computer Vision (iCCV), Kyoto, Japan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Aldoma, A.A., Vincze, M., Blodow, N., Gossow, D., Gedikli, S., Rusu, R.B., and Bradski, G. (2011, January 6\u201313). CAD-model recognition and 6DOF pose estimation using 3D cues. Proceedings of the 2011 IEEE International Conference Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130296"},{"key":"ref_4","unstructured":"Tombari, F., Salti, S., and di Stefano, L. (2010). Computer Vision\u2013ECCV 2010, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Haselirad, A., and Neubert, J. (2015, January 24\u201328). A novel Kinect-based system for 3D moving object interception with a 5-DOF robotic arm. Proceedings of the IEEE International Conference on Robotics and Automation, Gothenburg, Sweden.","DOI":"10.1109\/CoASE.2015.7294049"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Luo, R.C., and Kuo, C.W. (2015, January 22\u201324). A Scalable Modular Architecture of 3D Object Acquisition for Manufacturing Automation. 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Available online: http:\/\/rll.berkeley.edu\/bigbird\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1969\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:27:17Z","timestamp":1760210837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/11\/1969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,23]]},"references-count":16,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2016,11]]}},"alternative-id":["s16111969"],"URL":"https:\/\/doi.org\/10.3390\/s16111969","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2016,11,23]]}}}