4WD Robot Posture Estimation by Radial Multi-View Visual Odometry

This chapter presents a four-wheel robot’s trajectory tracking model by an extended Kalman filter (EKF) estimator for visual odometry using a divergent trinocular visual sensor. The trinocular sensor is homemade and a specific observer model was developed to measure 3D key-points by combining multi-view cameras. The observer approaches a geometric model and the key-points are used as references for estimating the robot’s displacement. The robot’s displacement is estimated by triangulation of multiple pairs of environmental 3D key-points. The four-wheel drive (4WD) robot’s inverse/direct kinematic control law is combined with the visual observer, the visual odometry model, and the EKF. The robot’s control law is used to produce experimental locomotion statistical variances and is used as a prediction model in the EKF. The proposed dead-reckoning approach models the four asynchronous drives and the four damping suspensions. This chapter presents the deductions of models, formulations and their validation, as well as the experimental results on posture state estimation comparing the four-wheel dead-reckoning model, the visual observer, and the EKF with an external global positioning reference

A Mirror-Based Active Vision System for Underwater Robots: From the Design to Active Object Tracking Application

A mirror-based active system capable of changing the view’s direction of a pre-existing fixed camera is presented. The aim of this research work is to extend the perceptual tracking capabilities of an underwater robot without altering its structure. The ability to control the view’s direction allows the robot to explore its entire surroundings without any actual displacement, which can be useful for more effective motion planning and for different navigation strategies, such as object tracking and/or obstacle evasion, which are of great importance for natural preservation in environments as complex and fragile as coral reefs. Active vision systems based on mirrors had been used mainly in terrestrial platforms to capture the motion of fast projectiles using high-speed cameras of considerable size and weight, but they had not been used on underwater platforms. In this sense, our approach incorporates a lightweight design adapted to an underwater robot using affordable and easy-access technology (i.e., 3D printing). Our active system consists of two arranged mirrors, one of which remains static in front of the robot’s camera, while the orientation of the second mirror is controlled by two servomotors. Object tracking is performed by using only the pixels contained on the homography of a defined area in the active mirror. HSV color space is used to reduce lighting change effects. Since color and geometry information of the tracking object are previously known, a window filter is applied over the H-channel for color blobs detection, then, noise is filtered and the object’s centroid is estimated. If the object is lost, a Kalman filter is applied to predict its position. Finally, with this information, an image PD controller computes the servomotor articular values. We have carried out experiments in real environments, testing our active vision system in an object-tracking application where an artificial object is manually displaced on the periphery of the robot and the mirror system is automatically reconfigured to keep such object focused by the camera, having satisfactory results in real time for detecting objects of low complexity and in poor lighting conditions

Distribución de una Red de Cámaras usando Algoritmos de Búsqueda Codiciosa y Genéticos

En este artículo se aborda el problema de la distribución de un conjunto de cámaras en un espacio de trabajo cerrado, con el objetivo de visualizar un área de interés en su totalidad en cada instante de tiempo. La metodología a seguir en este proyecto se basa en modelos de geometría computacional y proyectiva, así como características de detección y algoritmos de búsqueda. La experimentación se realiza empleando la misma metodología, variando únicamente los algoritmos de búsqueda que se implementarán, siendo estos algoritmos genéticos y búsqueda codiciosa (greedy search en inglés), con la finalidad de realizar una comparación de los resultados obtenidos con cada uno de los algoritmos mencionados, evaluando la cobertura del área de interés obtenida, así como el tiempo de computo involucrado en el análisis

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit

Congreso Internacional de Responsabilidad Social Apuestas para el desarrollo regional.

Congreso Internacional de Responsabilidad Social: apuestas para el desarrollo regional [Edición 1 / Nov. 6 - 7: 2019 Bogotá D.C.]El Congreso Internacional de Responsabilidad Social “Apuestas para el Desarrollo Regional”, se llevó a cabo los días 6 y 7 de noviembre de 2019 en la ciudad de Bogotá D.C. como un evento académico e investigativo liderado por la Corporación Universitaria Minuto de Dios -UNIMINUTO – Rectoría Cundinamarca cuya pretensión fue el fomento de nuevos paradigmas, la divulgación de conocimiento renovado en torno a la Responsabilidad Social; finalidad adoptada institucionalmente como postura ética y política que impacta la docencia, la investigación y la proyección social, y cuyo propósito central es la promoción de una “sensibilización consciente y crítica ante las situaciones problemáticas, tanto de las comunidades como del país, al igual que la adquisición de unas competencias orientadas a la promoción y al compromiso con el desarrollo humano y social integral”. (UNIMINUTO, 2014). Dicha postura, de conciencia crítica y sensibilización social, sumada a la experiencia adquirida mediante el trabajo articulado con otras instituciones de índole académico y de forma directa con las comunidades, permitió establecer como objetivo central del evento la reflexión de los diferentes grupos de interés, la gestión de sus impactos como elementos puntuales que contribuyeron en la audiencia a la toma de conciencia frente al papel que se debe asumir a favor de la responsabilidad social como aporte seguro al desarrollo regional y a su vez al fortalecimiento de los Objetivos de Desarrollo Sostenible

Statistics of visual and partial range data for mobile robot environment modeling : Luz Abril Torres Méndez.

This thesis presents a statistical learning framework for inferring geometric structures from images. Specifically, the proposed framework computes dense range maps of location sin the environment using only intensity images and very limited amount of range data as an input. This is achieved by integrating and analyzing the statistical relationships between the visual data and the available depth on terms of small patches. The scientific issue is to represent this correlation such that it can be used to recover range data where missing. Markov Random Fields are used as a model to capture the local statistics of the intensity and range.Experiments on real-world data are conducted under different configurations to demonstrate the feasibility of the method. In particular, our application is in mobile robotics, where inferring the 3D layout of indoor environments is a critical problem for achieving exploration and navigation tasks. The modeling of a large-scale environment involves the acquisition of a huge amount of range data to extract the geometry of the scene, and is often performed using sophisticated but costly hardware solutions. This task is physically demanding and time consuming for many real systems. By using the proposed framework, it is demonstrated that we can learn the geometric characteristics of the environment from the incomplete sensory data to build a 3D model of it.The contributions of this thesis are mainly three: First, it demonstrates the viability of the use of very limited range data together with intensity to recover complete dense range maps. Second, it presents a complete framework for building a 3D model of an indoor environment using a mobile robot. And third, it analyses and outlines the advantages and limitations encountered when dealing with large indoor environments.An additional contribution is the use of the method we propose for range estimation to an alternative problem: color correction and augmentation with the specific application to underwater images

A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs

One of the most important tasks when creating a map of visual information obtained from different agents is finding common locations between the sets of images that enable them to be fused into a single representation. Typical approaches focus on images obtained from the same agent. However, in this paper, we focus on recognizing the same places in images captured by different agents to create a topological map of coral reefs. The main components of the proposed method are the voting scheme to find a sparse similarity matrix between different frames and an effective method to match sequences of images exploiting the sparsity of the resulting similarity matrix. We have applied our method to sequences of images obtained from coral reef explorations performed by different agents. The presented method shows a good performance compared to other well-established methods such as FABMAP. This demonstrates its ability to find common locations from visual information gathered from different sources, which eases the collaboration between humans and robots to map the environment