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Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time.

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Nanoparticles (NPs) have been widely used as target delivery vehicles for therapeutic goods; however, compared with inorganic and organic nanomaterials, protein nanomaterials have better biocompatibility and can self-assemble into highly ordered cage-like structures, which are more favorable for applications in targeted drug delivery. In this review, we concentrate on the typical protein cage nanoparticles drugs encapsulation processes, such as drug fusion expression, diffusion, electrostatic contact, covalent binding, and protein cage disassembly/recombination. The usage of protein cage nanoparticles in biomedicine is also briefly discussed. These materials can be utilized to transport small molecules, peptides, siRNA, and other medications for anti-tumor, contrast, etc.