An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model

In this work, we propose a novel procedure for video super-resolution, that is the recovery of a sequence of high-resolution images from its low-resolution counterpart. Our approach is based on a "sequential" model (i.e., each high-resolution frame is supposed to be a displaced version of the preceding one) and considers the use of sparsity-enforcing priors. Both the recovery of the high-resolution images and the motion fields relating them is tackled. This leads to a large-dimensional, non-convex and non-smooth problem. We propose an algorithmic framework to address the latter. Our approach relies on fast gradient evaluation methods and modern optimization techniques for non-differentiable/non-convex problems. Unlike some other previous works, we show that there exists a provably-convergent method with a complexity linear in the problem dimensions. We assess the proposed optimization method on {several video benchmarks and emphasize its good performance with respect to the state of the art.}Comment: 37 pages, SIAM Journal on Imaging Sciences, 201

Optimal modelling and experimentation for the improved sustainability of microfluidic chemical technology design

Optimization of the dynamics and control of chemical processes holds the promise of improved sustainability for chemical technology by minimizing resource wastage. Anecdotally, chemical plant may be substantially over designed, say by 35-50%, due to designers taking account of uncertainties by providing greater flexibility. Once the plant is commissioned, techniques of nonlinear dynamics analysis can be used by process systems engineers to recoup some of this overdesign by optimization of the plant operation through tighter control. At the design stage, coupling the experimentation with data assimilation into the model, whilst using the partially informed, semi-empirical model to predict from parametric sensitivity studies which experiments to run should optimally improve the model. This approach has been demonstrated for optimal experimentation, but limited to a differential algebraic model of the process. Typically, such models for online monitoring have been limited to low dimensions. Recently it has been demonstrated that inverse methods such as data assimilation can be applied to PDE systems with algebraic constraints, a substantially more complicated parameter estimation using finite element multiphysics modelling. Parametric sensitivity can be used from such semi-empirical models to predict the optimum placement of sensors to be used to collect data that optimally informs the model for a microfluidic sensor system. This coupled optimum modelling and experiment procedure is ambitious in the scale of the modelling problem, as well as in the scale of the application - a microfluidic device. In general, microfluidic devices are sufficiently easy to fabricate, control, and monitor that they form an ideal platform for developing high dimensional spatio-temporal models for simultaneously coupling with experimentation. As chemical microreactors already promise low raw materials wastage through tight control of reagent contacting, improved design techniques should be able to augment optimal control systems to achieve very low resource wastage. In this paper, we discuss how the paradigm for optimal modelling and experimentation should be developed and foreshadow the exploitation of this methodology for the development of chemical microreactors and microfluidic sensors for online monitoring of chemical processes. Improvement in both of these areas bodes to improve the sustainability of chemical processes through innovative technology. (C) 2008 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved

Decentralized formation pose estimation for spacecraft swarms

For spacecraft swarms, the multi-agent localization algorithm must scale well with the number of spacecraft and adapt to time-varying communication and relative sensing networks. In this paper, we present a decentralized, scalable algorithm for swarm localization, called the Decentralized Pose Estimation (DPE) algorithm. The DPE considers both communication and relative sensing graphs and defines an observable local formation. Each spacecraft jointly localizes its local subset of spacecraft using direct and communicated measurements. Since the algorithm is local, the algorithm complexity does not grow with the number of spacecraft in the swarm. As part of the DPE, we present the Swarm Reference Frame Estimation (SRFE) algorithm, a distributed consensus algorithm to co-estimate a common Local-Vertical, Local-Horizontal (LVLH) frame. The DPE combined with the SRFE provides a scalable, fully-decentralized navigation solution that can be used for swarm control and motion planning. Numerical simulations and experiments using Caltech’s robotic spacecraft simulators are presented to validate the effectiveness and scalability of the DPE algorithm

Decentralized Formation Pose Estimation for Spacecraft Swarms

For spacecraft swarms, the multi-agent localization algorithm must scale well with the number of spacecraft and adapt to time-varying communication and relative sensing networks. In this paper, we present a decentralized, scalable algorithm for swarm localization, called the Decentralized Pose Estimation (DPE) algorithm. The DPE considers both communication and relative sensing graphs and defines an observable local formation. Each spacecraft jointly localizes its local subset of spacecraft using direct and communicated measurements. Since the algorithm is local, the algorithm complexity does not grow with the number of spacecraft in the swarm. As part of the DPE, we present the Swarm Reference Frame Estimation (SRFE) algorithm, a distributed consensus algorithm to co-estimate a common Local-Vertical, Local-Horizontal (LVLH) frame. The DPE combined with the SRFE provides a scalable, fully-decentralized navigation solution that can be used for swarm control and motion planning. Numerical simulations and experiments using Caltech’s robotic spacecraft simulators are presented to validate the effectiveness and scalability of the DPE algorithm

A multi-hypothesis approach for range-only simultaneous localization and mapping with aerial robots

Los sistemas de Range-only SLAM (o RO-SLAM) tienen como objetivo la construcción de un mapa formado por la posición de un conjunto de sensores de distancia y la localización simultánea del robot con respecto a dicho mapa, utilizando únicamente para ello medidas de distancia. Los sensores de distancia son dispositivos capaces de medir la distancia relativa entre cada par de dispositivos. Estos sensores son especialmente interesantes para su applicación a vehículos aéreos debido a su reducido tamaño y peso. Además, estos dispositivos son capaces de operar en interiores o zonas con carencia de señal GPS y no requieren de una línea de visión directa entre cada par de dispositivos a diferencia de otros sensores como cámaras o sensores laser, permitiendo así obtener una lectura de datos continuada sin oclusiones. Sin embargo, estos sensores presentan un modelo de observación no lineal con una deficiencia de rango debido a la carencia de información de orientación relativa entre cada par de sensores. Además, cuando se incrementa la dimensionalidad del problema de 2D a 3D para su aplicación a vehículos aéreos, el número de variables ocultas del modelo aumenta haciendo el problema más costoso computacionalmente especialmente ante implementaciones multi-hipótesis. Esta tesis estudia y propone diferentes métodos que permitan la aplicación eficiente de estos sistemas RO-SLAM con vehículos terrestres o aéreos en entornos reales. Para ello se estudia la escalabilidad del sistema en relación al número de variables ocultas y el número de dispositivos a posicionar en el mapa. A diferencia de otros métodos descritos en la literatura de RO-SLAM, los algoritmos propuestos en esta tesis tienen en cuenta las correlaciones existentes entre cada par de dispositivos especialmente para la integración de medidas estÃa˛ticas entre pares de sensores del mapa. Además, esta tesis estudia el ruido y las medidas espúreas que puedan generar los sensores de distancia para mejorar la robustez de los algoritmos propuestos con técnicas de detección y filtración. También se proponen métodos de integración de medidas de otros sensores como cámaras, altímetros o GPS para refinar las estimaciones realizadas por el sistema RO-SLAM. Otros capítulos estudian y proponen técnicas para la integración de los algoritmos RO-SLAM presentados a sistemas con múltiples robots, así como el uso de técnicas de percepción activa que permitan reducir la incertidumbre del sistema ante trayectorias con carencia de trilateración entre el robot y los sensores de destancia estáticos del mapa. Todos los métodos propuestos han sido validados mediante simulaciones y experimentos con sistemas reales detallados en esta tesis. Además, todos los sistemas software implementados, así como los conjuntos de datos registrados durante la experimentación han sido publicados y documentados para su uso en la comunidad científica