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dc.contributor.authorHe, Hongsheng
dc.contributor.authorYan, Fujian
dc.contributor.authorWang, Dali
dc.identifier.citationF. Yan, D. Wang and H. He, "Comprehension of Spatial Constraints by Neural Logic Learning from a Single RGB-D Scan," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 9008-9013, doi: 10.1109/IROS51168.2021.9635939.en_US
dc.descriptionClick on the DOI link to view this conference paper (may not be free).en_US
dc.description.abstractAutonomous industrial assembly relies on the precise measurement of spatial constraints as designed by computer-aided design (CAD) software such as SolidWorks. This paper proposes a framework for an intelligent industrial robot to understand the spatial constraints for model assembly. An extended generative adversary network (GAN) with a 3D long short-term memory (LSTM) network was designed to composite 3D point clouds from a single RGB-D scan. The spatial constraints of the segmented point clouds are identified by a neural-logic network that incorporates general knowledge of spatial constraints in terms of first-order logic. The model was designed to comprehend a complete set of spatial constraints that are consistent with industrial CAD software, including left, right, above, below, front, behind, parallel, perpendicular, concentric, and coincident relations. The accuracy of 3D model composition and spatial constraint identification was evaluated by the RGB-D scans and 3D models in the ABC dataset. The proposed model achieved 57.23% intersection over union (IoU) in 3D model composition, and over 99% in comprehending all spatial constraints.en_US
dc.relation.ispartofseries2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2021
dc.subjectSpatial constraintsen_US
dc.subjectNeural-logic learningen_US
dc.subjectLogic rulesen_US
dc.titleComprehension of spatial constraints by neural logic learning from a single RGB-D scanen_US
dc.typeConference paperen_US
dc.rights.holder©2021 IEEEen_US

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