3D registration and integrated segmentation framework for heterogeneous unmanned robotic systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors' measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds' alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments

Semi-Automated 3D Registration for Heterogeneous Unmanned Robots Based on Scale Invariant Method

This paper addresses the problem of 3D registration of outdoor environments combining heterogeneous datasets acquired from unmanned aerial (UAV) and ground (UGV) vehicles. In order to solve this problem, we introduced a novel Scale Invariant Registration Method (SIRM) for semi-automated registration of 3D point clouds. The method is capable of coping with an arbitrary scale difference between the point clouds, without any information about their initial position and orientation. Furthermore, the SIRM does not require having a good initial overlap between two heterogeneous datasets. Our method strikes an elegant balance between the existing fully automated 3D registration systems (which often fail in the case of heterogeneous datasets and harsh outdoor environments) and fully manual registration approaches (which are labour-intensive). The experimental validation of the proposed 3D heterogeneous registration system was performed on large-scale datasets representing unstructured and harsh outdoor environments, demonstrating the potential and benefits of the proposed 3D registration system in real-world environments

Fast Iterative 3D Mapping for Large-Scale Outdoor Environments with Local Minima Escape Mechanism

This paper introduces a novel iterative 3D mapping framework for large scale natural terrain and complex environments. The framework is based on an Iterative-Closest-Point (ICP) algorithm and an iterative error minimization mechanism, allowing robust 3D map registration. This was accomplished by performing pairwise scan registrations without any prior known pose estimation information and taking into account the measurement uncertainties due to the 6D coordinates (translation and rotation) deviations in the acquired scans. Since the ICP algorithm does not guarantee to escape from local minima during the mapping, new algorithms for the local minima estimation and local minima escape process were proposed. The proposed framework is validated using large scale field test data sets. The experimental results were compared with those of standard, generalized and non-linear ICP registration methods and the performance evaluation is presented, showing improved performance of the proposed 3D mapping framework

Fast Statistical Outlier Removal Based Method for Large 3D Point Clouds of Outdoor Environments

This paper proposes a very effective method for data handling and preparation of the input 3D scans acquired from laser scanner mounted on the Unmanned Ground Vehicle (UGV). The main objectives are to improve and speed up the process of outliers removal for large-scale outdoor environments. This process is necessary in order to filter out the noise and to downsample the input data which will spare computational and memory resources for further processing steps, such as 3D mapping of rough terrain and unstructured environments. It includes the Voxel-subsampling and Fast Cluster Statistical Outlier Removal (FCSOR) subprocesses. The introduced FCSOR represents an extension on the Statistical Outliers Removal (SOR) method which is effective for both homogeneous and heterogeneous point clouds. This method is evaluated on real data obtained in outdoor environment

Epidemiology of enterotoxigenic Escherichia coli and impact on the growth of children in the first two years of life in Lima, Peru

BackgroundEnterotoxigenic E. coli (ETEC) is a leading cause of diarrheal morbidity and mortality in children, although the data on disease burden, epidemiology, and impact on health at the community level are limited.MethodsIn a longitudinal birth cohort study of 345 children followed until 24 months of age in Lima, Peru, we measured ETEC burden in diarrheal and non-diarrheal samples using quantitative PCR (LT, STh, and STp toxin genes), studied epidemiology and measured anthropometry in children.ResultsAbout 70% of children suffered from one or more ETEC diarrhea episodes. Overall, the ETEC incidence rate (IR) was 73 per 100 child-years. ETEC infections began early after birth causing 10% (8.9–11.1) ETEC-attributable diarrheal burden at the population level (PAF) in neonates and most of the infections (58%) were attributed to ST-ETEC [PAF 7.9% (1.9–13.5)] and LT + ST-ETEC (29%) of which all the episodes were associated with diarrhea. ETEC infections increased with age, peaking at 17% PAF (4.6–27.7%; p = 0.026) at 21 to 24 months. ST-ETEC was the most prevalent type (IR 32.1) with frequent serial infections in a child. The common colonization factors in ETEC diarrhea cases were CFA/I, CS12, CS21, CS3, and CS6, while in asymptomatic ETEC cases were CS12, CS6 and CS21. Only few (5.7%) children had repeated infections with the same combination of ETEC toxin(s) and CFs, suggested genotype-specific immunity from each infection. For an average ETEC diarrhea episode of 5 days, reductions of 0.060 weight-for-length z-score (0.007 to 0.114; p = 0.027) and 0.061 weight-for-age z-score (0.015 to 0.108; p = 0.009) were noted in the following 30 days.ConclusionThis study showed that ETEC is a significant pathogen in Peruvian children who experience serial infections with multiple age-specific pathotypes, resulting in transitory growth impairment

2022 ESC guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: developed by the task force for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death of the European Society of Cardiology (ESC) endorsed by the Association for European Paediatric and Congenital Cardiology (A

Cardiolog

Extrapolation-based approach to optimization with constraints determined by the Robin boundary problem for the Laplace equation

This paper considers the application of extrapolation techniques in finding approximate solutions of some optimization problems with constraints defined by the Robin boundary problem for the Laplace equation. When applied extrapolation techniques produce very accurate solutions of the boundary problems on relatively coarse meshes, but this paper demonstrates that this is not a real restriction when dealing with optimization problems. Producing a solution of continuous problem by polynomial extrapolation based on the low-order discrete problem solutions significantly reduces both computational time and memory. The present paper illustrates this approach using finite-difference and finite-element methods, and finally makes a brief remark about some tacit engineering assumptions regarding numerical solutions of conductive media problems by construction of equivalent resistor networks