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Venous blood collection is a widely used medical diagnostic technique, and with rapid advancements in robotics, robotic venous blood collection has the potential to replace traditional manual methods. The success of this robotic approach is heavily dependent on the quality of vein imaging. In this paper, we develop a vein imaging device based on the simulation analysis of vein imaging parameters and propose a U-Net+ResNet18 neural network for vein image segmentation. The U-Net+ResNet18 neural network integrates the residual blocks from ResNet18 into the encoder of the U-Net to form a new neural network. ResNet18 is pre-trained using the Bootstrap Your Own Latent (BYOL) framework, and its encoder parameters are transferred to the U-Net+ResNet18 neural network, enhancing the segmentation performance of vein images with limited labelled data. Furthermore, we optimize the AD-Census stereo matching algorithm by developing a variable-weight version, which improves its adaptability to image variations across different regions. Results show that, compared to U-Net, the BYOL+U-Net+ResNet18 method achieves an 8.31% reduction in Binary Cross-Entropy (BCE), a 5.50% reduction in Hausdorff Distance (HD), a 15.95% increase in Intersection over Union (IoU), and a 9.20% increase in the Dice coefficient (Dice), indicating improved image segmentation quality. The average error of the optimized AD-Census stereo matching algorithm is reduced by 25.69%, and the improvement of the image stereo matching performance is more obvious. Future research will explore the application of the vein imaging system in robotic venous blood collection to facilitate real-time puncture guidance.

In response to the challenge of handling large-scale 3D point cloud data, downsampling is a common approach, yet it often leads to the problem of feature loss. We present a dynamic downsampling algorithm for 3D point cloud maps based on an improved voxel filtering approach. The algorithm consists of two modules, namely, dynamic downsampling and point cloud edge extraction. The former adapts voxel downsampling according to the features of the point cloud, while the latter preserves edge information within the 3D point cloud map. Comparative experiments with voxel downsampling, grid downsampling, clustering-based downsampling, random downsampling, uniform downsampling, and farthest-point downsampling were conducted. The proposed algorithm exhibited favorable downsampling simplification results, with a processing time of 0.01289 s and a simplification rate of 91.89%. Additionally, it demonstrated faster downsampling speed and showcased improved overall performance. This enhancement not only benefits productivity but also highlights the system’s efficiency and effectiveness.

Extracting entity, relation, and attribute information from unstructured text is crucial for constructing large-scale knowledge graphs (KG). Existing research approaches either focus on entity recognition before relation extraction or employ unified annotation. However, these methods overlook the intrinsic relation between entity recognition and relation extraction, resulting in ineffective handling of triple overlap issues where multiple relations share the same entity in a sentence. To address these challenges, this paper proposes an entity-relation joint extraction model comprising two independent sub-modules: one for extracting the head entity and the other for extracting the tail entity and its corresponding relation. The model generates candidate entities and relations by enumerating token sequences in sentences, and then uses the two sub-modules to predict entities and relations. The predicted entities and relations are jointly decoded to obtain relational triples, avoiding error propagation and solving redundancy, entity overlap, and poor generalization. Extensive experiments demonstrate that our model achieves state-of-the-art performance on WebNLG, NYT, WebNLG*, and NYT* public benchmarks. It outperforms all baselines on the WebNLG* dataset, showing significant improvements in different types of triples: normal, SEO, and EPO by 3.8%, 2.9%, and 5.5%, respectively, compared to ETL-Span. For the NYT* dataset, our method improves by 5.7% in triples of Normal type, thereby confirming its effectiveness.