LiDAR Enhanced Structure-from-Motion

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.Comment: 6 pages plus reference. Work has been submitted to ICRA 202

Feasibility of discriminating UAV propellers noise from distress signals to locate people in enclosed environments using MEMS microphone arrays

Producción CientíficaDetecting and finding people are complex tasks when visibility is reduced. This happens, for example, if a fire occurs. In these situations, heat sources and large amounts of smoke are generated. Under these circumstances, locating survivors using thermal or conventional cameras is not possible and it is necessary to use alternative techniques. The challenge of this work was to analyze if it is feasible the integration of an acoustic camera, developed at the University of Valladolid, on an unmanned aerial vehicle (UAV) to locate, by sound, people who are calling for help, in enclosed environments with reduced visibility. The acoustic array, based on MEMS (micro-electro-mechanical system) microphones, locates acoustic sources in space, and the UAV navigates autonomously by closed enclosures. This paper presents the first experimental results locating the angles of arrival of multiple sound sources, including the cries for help of a person, in an enclosed environment. The results are promising, as the system proves able to discriminate the noise generated by the propellers of the UAV, at the same time it identifies the angles of arrival of the direct sound signal and its first echoes reflected on the reflective surfaces.Junta de Castilla y León (project VA082G18

Association between admission-blood-glucose-to-albumin ratio and clinical outcomes in patients with ST-elevation myocardial infarction undergoing percutaneous coronary intervention

IntroductionIt is unclear whether admission-blood-glucose-to-albumin ratio (AAR) predicts adverse clinical outcomes in patients with ST-segment elevation myocardial infarction (STEMI) who are treated with percutaneous coronary intervention (PCI). Here, we performed a observational study to explore the predictive value of AAR on clinical outcomes.MethodsPatients diagnosed with STEMI who underwent PCI between January 2010 and February 2020 were enrolled in the study. The patients were classified into three groups according to AAR tertile. The primary outcome was in-hospital all-cause mortality, and the secondary outcomes were in-hospital major adverse cardiac events (MACEs), as well as all-cause mortality and MACEs during follow-up. Logistic regression, Kaplan–Meier analysis, and Cox proportional hazard regression were the primary analyses used to estimate outcomes.ResultsAmong the 3,224 enrolled patients, there were 130 cases of in-hospital all-cause mortality (3.9%) and 181 patients (5.4%) experienced MACEs. After adjustment for covariates, multivariate analysis demonstrated that an increase in AAR was associated with an increased risk of in-hospital all-cause mortality [adjusted odds ratio (OR): 2.72, 95% CI: 1.47–5.03, P = 0.001] and MACEs (adjusted OR: 1.91, 95% CI: 1.18–3.10, P = 0.009), as well as long-term all-cause mortality [adjusted hazard ratio (HR): 1.64, 95% CI: 1.19–2.28, P = 0.003] and MACEs (adjusted HR: 1.58, 95% CI: 1.16–2.14, P = 0.003). Receiver operating characteristic (ROC) curve analysis indicated that AAR was an accurate predictor of in-hospital all-cause mortality (AUC = 0.718, 95% CI: 0.675–0.761) and MACEs (AUC = 0.672, 95% CI: 0.631–0.712).DiscussionAAR is a novel and convenient independent predictor of all-cause mortality and MACEs, both in-hospital and long-term, for STEMI patients receiving PCI