Deformable Linear Objects 3D Shape Estimation and Tracking From Multiple 2D Views

This letter presents DLO3DS , an approach for the 3D shapes estimation and tracking of Deformable Linear Objects (DLOs) such as cables, wires or plastic hoses, using a cheap and compact 2D vision sensor mounted on the robot end-effector. DLO3DS can be applied in all those scenarios in which the perception and manipulation of DLO-like structures are needed, such as in the case of switchgear cabling, wiring harness manufacturing and assembly in the automotive and aerospace industries, or production of hoses for medical applications. The developed procedure is based on a pipeline that first processes the images coming from the 2D camera extracting key topological points along the DLOs. These points are then used to model each DLO with a B-spline curve. Finally, the set of splines obtained from all the images is matched by exploiting a multi-view stereo-based algorithm. DLO3DS is validated both on a real scenario and on simulated data obtained by exploiting a rendering engine for photo-realistic images. In this way, reliable ground-truth data are retrieved and utilized for assessing the estimation error achievable by DLO3DS , which on the employed test set is characterized by a mean reconstruction error of 0.82 mm

Cable Detection and Manipulation for DLO-in-Hole Assembly Tasks

This paper describes a cyber-physical system for the manipulation of Deformable Linear Objects (DLOs) addressing the DLO-in-hole insertion problem targeting an industrial sce-nario, the switchgear's components cabling task. In particular, the task considered is the insertion of DLOs in the switchgear components' holes. This task is very challenging since a precise knowledge of the DLO tip position and orientation is required for a successful operation. We tackled the DLO-in-hole problem from the computer vision perspective constraining our setup on employing just simple 2D images and by using the mobility of the robotic arm for achieving the full 3D knowledge of the DLOs. Then, the DLO tip is detected from two different image planes and the robot's trajectory corrected accordingly before insertion. To prove the effectiveness of the proposed solution, an example scenario is prepared and the method validated experimentally attempting the insertion of several DLOs in a sample switchgear component, obtaining an overall insertion success rate of 82.5 %

RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation

Deformable Linear Objects (DLOs) such as cables, wires, ropes, and elastic tubes are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically segmented from the background. Afterward, a novel method to separate the DLO instances is applied. It employs the generation of a graph representation of the scene given the semantic mask where the graph nodes are sampled from the DLOs center-lines whereas the graph edges are selected based on topological reasoning. RT-DLO is experimentally evaluated against both DLO-specific and general-purpose instance segmentation deep learning approaches, achieving overall better performances in terms of accuracy and inference time

REMODEL. WP4. Vision Based Perception. T4_3. Cable Detection And Tracking. Electric Wires Dataset. Training and Test sets for Image Segmentation. v0

The dataset contains data for semantic segmentation of electric wires with domain independence, generated in the framework of REMODEL project. The dataset is automatically generated using chroma-key technique and contains 57300 images (where 28650 are RGB images and the other 28650 are the corresponding ground truth binary masks)

An extracellular vesicle epitope profile is associated with acute myocardial infarction

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Risk stratification of patients with SARS-CoV-2 by tissue factor expression in circulating extracellular vesicles

Inflammatory response following SARS-CoV-2 infection results in substantial increase of amounts of intravascular pro-coagulant extracellular vesicles (EVs) expressing tissue factor (CD142) on their surface. CD142-EV turned out to be useful as diagnostic biomarker in COVID-19 patients. Here we aimed at studying the prognostic capacity of CD142-EV in SARS-CoV-2 infection. Expression of CD142-EV was evaluated in 261 subjects admitted to hospital for pneumonia and with a positive molecular test for SARS-CoV-2. The study population consisted of a discovery cohort of selected patients (n = 60) and an independent validation cohort including unselected consecutive enrolled patients (n = 201). CD142-EV levels were correlated with post-hospitalization course of the disease and compared to the clinically available 4C Mortality Score as referral. CD142-EV showed a reliable performance to predict patient prognosis in the discovery cohort (AUC = 0.906) with an accuracy of 81.7%, that was confirmed in the validation cohort (AUC = 0.736). Kaplan-Meier curves highlighted a high discrimination power in unselected subjects with CD142-EV being able to stratify the majority of patients according to their prognosis. We obtained a comparable accuracy, being not inferior in terms of prediction of patients' prognosis and risk of mortality, with 4C Mortality Score. The expression of surface vesicular CD142 and its reliability as prognostic marker was technically validated using different immunocapture strategies and assays. The detection of CD142 on EV surface gains considerable interest as risk stratification tool to support clinical decision making in COVID-19