Monte Carlo localization for teach-and-repeat feature-based navigation

This work presents a combination of a teach-and-replay visual navigation and Monte Carlo localization methods. It improves a reliable teach-and-replay navigation method by replacing its dependency on precise dead-reckoning by introducing Monte Carlo localization to determine robot position along the learned path. In consequence, the navigation method becomes robust to dead-reckoning errors, can be started from at any point in the map and can deal with the `kidnapped robot' problem. Furthermore, the robot is localized with MCL only along the taught path, i.e. in one dimension, which does not require a high number of particles and significantly reduces the computational cost. Thus, the combination of MCL and teach-and-replay navigation mitigates the disadvantages of both methods. The method was tested using a P3-AT ground robot and a Parrot AR.Drone aerial robot over a long indoor corridor. Experiments show the validity of the approach and establish a solid base for continuing this work

A practical multirobot localization system

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems

Estimativa da covariância ICP para a localização de um robô diferencial usando Odometria e varredura a laser

In this work we present a probabilistic method to solve the localization problem of a differential robot. The Extended Kalman Filter (EKF) is used to merge the information obtained from ICP (Iterative Closest Point) laser measurement records with the odometry information provided by encoders. To use EKF it is necessary to estimate the covariance of each information source, however the ICP algorithm does not return the associated covariance. This paper describes a way to calculate this covariance. The results obtained show that the sensor fusion method results in a more accurate estimation of the robot's pose compared to the estimations obtained through odometry and ICP individually.En este trabajo se presenta un método probabilístico para resolver el problema de la localización de un robot diferencial. Se usa el Filtro Extendido de Kalman (EKF) para fusionar la información obtenida por registraciones de mediciones láser mediante ICP (IterativeClosest Point) con la información de odometría provista por encoders. Para utilizar EKF es necesario estimar la covarianza de cada fuente de información, sin embargo el algoritmo ICP no devuelve la covarianza asociada. En este artículo se describe una forma de calcular esta covarianza. Los resultados obtenidos muestran que el método de fusión de sensores resulta en una estimación más precisa de la pose del robot en comparación con las estimaciones que se podrían obtener mediante odometría e ICP individualmente.Neste trabalho apresentamos um método probabilístico para resolver o problema da localização de um robô diferencial. O Extended Kalman Filter (EKF) é usado para mesclar as informações obtidas pelos registros de medição a laser pelo ICP (IterativeClosest Point) com as informações de odometria fornecidas pelos codificadores. Para usar o EKF é necessário estimar a covariância de cada fonte de informação, porém o algoritmo ICP não retorna a covariância associada. Este artigo descreve uma maneira de calcular essa covariância. Os resultados obtidos mostram que o método de fusão de sensores resulta em uma estimativa mais precisa da postura do robô em relação às estimativas que poderiam ser obtidas por odometria e ICP individualmente

Accelerating DFT calculations using the Graphical Processor Unit

Abstract In this work we show how GPGPU, or General Processing on the Graphical Processor Unit, can be useful on scientific computing. In particular, we applied this technology to electronic structure calculations performed at the density functional theory (DFT) level. We started from an existing chemistry simulation program, and selectively replaced portions of it with GPU-oriented code. In energy calculations over fixed geometries, we achieved speedups of up to approximately five times, compared to the CPU version

Burnout en médicos residentes de especialidades y subespecialidades: estudio de prevalencia y variables asociadas en un centro universitario

Introducción: El burnout es un síndrome caracterizado por agotamiento emocional, despersonalización y bajo sentido de logro personal. Los médicos residentes de especialidad y subespecialidad constituyen una población de riesgo por la alta carga laboral y la interferencia con su vida personal. Nuestro objetivo fue evaluar la prevalencia de burnout y su asociación con variables sociodemográficas, en residentes de especialidad y subespecialidad dela Pontificia Universidad Católica de Chile (PUC). Métodos: Se realizó una encuesta electrónica a los residentes de especialidad y subespecialidad de la PUC, que incluyó el “Inventario de Burnout de Maslach” (22 preguntas divididas en 3 dimensiones). Se sumaron los puntos de cada dimensión y se clasificó a los residentes en riesgo de burnout al presentar altos índices de agotamiento emocional y/o despersonalización. El análisis estadístico incluyó un análisis univariado y multivariado. Resultados: 415 encuestas fueron contestadas (tasa de respuesta 86%). El 38,3% de los residentes cumplió criterios de burnout, con un 41,9% en residentes de especialidad y 24,1% en residentes de subespecialidad. En el análisis por subgrupos, la mayor prevalencia se encontró en especialidades quirúrgicas (55,3%). Los residentes extranjeros, los programas de especialidad (comparados con subespecialidad) y los programas de especialidades quirúrgicas se asociaron de manera independiente a burnout (OR 3,8 IC95% 1,4-10,5, p=0,01; OR 2,3 IC95% 1,3-4,1, p<0,01 y OR 1,7 IC95% 1,1-2,7; p=0,02, respectivamente). La carga laboral horaria no se asoció de manera independiente a burnout (p=0,19). Conclusión: Los residentes de especialidad y subespecialidad presentan una alta prevalencia de burnout. Adicionalmente, ser extranjero, el pertenecer a un programa de especialidad y los programas de especialidades quirúrgicas se asocian de manera independiente a burnout