Structure-Aware Denoising for Real-world Noisy Point Clouds with Complex Structures

Sun, Guoxing; Chu, Chao; Mei, Jialin; Li, Weiqing; Su, Zhiyong

doi:10.1016/j.cad.2022.103275

Cited by 7 publications

(1 citation statement)

References 44 publications

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“…The 3D point cloud pickup methods are also in the research stage. The difficulty is that the processing method of the 3D point cloud is compared to the 2D machine vision method, hardware requirements are higher, and there are high restrictions in complex environments [14]. In the ideal laboratory environment, many studies have conducted research on point cloud pickup methods for collaborative robots.…”

Section: Introductionmentioning

confidence: 99%

A 3D pickup estimation method for industrial parallel robots based on point cloud simplification and registration

Zhang,

Wang,

Wang

et al. 2023

Preprint

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In industrial environments, cameras are strongly affected by light and viewpoints, and parallel robots based on traditional vision methods have poor sorting abilities. In two-dimensional vision, depth information cannot be estimated, and parallel robots can only pick up objects based on the fixed height. In this paper, we propose a 3D pickup estimation method for parallel robots based on point cloud simplification and registration for multi-objective pickup tasks. Firstly, a point cloud segmentation method based on the spatial domain is proposed to separate incomplete object point clouds from robot space. The 3D scanner scans the complete object point clouds to produce the object dataset. Secondly, according to the fast point feature histogram (FPFH) and the weight locally optimal projection (WLOP), a fusing the FPFH and WLOP (FF-WLOP) method is proposed to simplify the incomplete point cloud and obtain more distinctive edge features of objects. The complete point cloud in the dataset is aligned with the simplified incomplete point cloud and the coordinate information of the calculated barycenter is given to the incomplete point cloud. Then a dynamic weight singular value decomposition (D-SVD) hand-eye calibration method and a best projection point strategy are proposed to transform the barycenter coordinates of the object to the best pickup coordinates. The experiment results show that the point cloud registration error is 0.38mm, the pickup rate is 92%, and the robot positioning error is 4.67mm, which meets the basic pickup requirements.

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Section: Introductionmentioning

confidence: 99%