File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Registration of point clouds for 3D shape inspection

TitleRegistration of point clouds for 3D shape inspection
Authors
Keywords3D shape inspection
ICP
Area-sensor-based robot hand-eye calibration
Point cloud registration
Issue Date2006
Citation
IEEE International Conference on Intelligent Robots and Systems, 2006, p. 235-240 How to Cite?
AbstractPoint cloud registration and sensor calibration are two critical technical issues concerning robot-mounted, area sensor systems. Iterative Closest Point (ICP)-based algorithms developed in the past are commonly used for point cloud registration. However, due to its least squared fitting nature, registration quality depends on how closely the measured part matches its nominal definition, typical in the form of a CAD model in modern times. To eliminate the dependency of registration quality on part closeness to the CAD model, we present, in this paper, a more robust approach based in a series of coordinate transformations. Geometric features and surface gradients are accounted for to improve the registration performance. To achieve robot/sensor hand-eye calibration, an ICP-based method is used. The reason is that this calibration step typically utilizes standard parts or gauges machined for the purpose of calibration, as such they are known shapes that match their CAD models with much tighter tolerances. This offers us an unique opportunity to apply an ICP-based tool to find the transformation matrix from the robot end effector to an area sensor mounted onto it. The discussed method was successfully implemented and tested in a feedback-based, robot-mounted area sensor system developed for manufacturing quality control of 3D freeform surfaces. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/212922

 

DC FieldValueLanguage
dc.contributor.authorShi, Quan-
dc.contributor.authorXi, Ning-
dc.contributor.authorChen, Yifan-
dc.contributor.authorSheng, Weihua-
dc.date.accessioned2015-07-28T04:05:27Z-
dc.date.available2015-07-28T04:05:27Z-
dc.date.issued2006-
dc.identifier.citationIEEE International Conference on Intelligent Robots and Systems, 2006, p. 235-240-
dc.identifier.urihttp://hdl.handle.net/10722/212922-
dc.description.abstractPoint cloud registration and sensor calibration are two critical technical issues concerning robot-mounted, area sensor systems. Iterative Closest Point (ICP)-based algorithms developed in the past are commonly used for point cloud registration. However, due to its least squared fitting nature, registration quality depends on how closely the measured part matches its nominal definition, typical in the form of a CAD model in modern times. To eliminate the dependency of registration quality on part closeness to the CAD model, we present, in this paper, a more robust approach based in a series of coordinate transformations. Geometric features and surface gradients are accounted for to improve the registration performance. To achieve robot/sensor hand-eye calibration, an ICP-based method is used. The reason is that this calibration step typically utilizes standard parts or gauges machined for the purpose of calibration, as such they are known shapes that match their CAD models with much tighter tolerances. This offers us an unique opportunity to apply an ICP-based tool to find the transformation matrix from the robot end effector to an area sensor mounted onto it. The discussed method was successfully implemented and tested in a feedback-based, robot-mounted area sensor system developed for manufacturing quality control of 3D freeform surfaces. © 2006 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE International Conference on Intelligent Robots and Systems-
dc.subject3D shape inspection-
dc.subjectICP-
dc.subjectArea-sensor-based robot hand-eye calibration-
dc.subjectPoint cloud registration-
dc.titleRegistration of point clouds for 3D shape inspection-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IROS.2006.281677-
dc.identifier.scopuseid_2-s2.0-34250662584-
dc.identifier.spage235-
dc.identifier.epage240-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats