Efficient global illumination calculation for inverse lighting problems

La luz es un elemento clave en la manera en que percibimos y experimentamos nuestro entorno. Como tal, es un objeto mas a modelar en el proceso de diseño, de forma similar a como ocurre con las formas y los materiales. Las intenciones de iluminacion (LI) son los objetivos y restricciones que el diseñador pretende alcanzar en el proceso del diseño de iluminaci´on: ¿qué superficies se deben iluminar con luz natural y cuales con luz artificial?, ¿qué zonas deben estar en sombra?, ¿cuales son las intensidades maximas y mínimas permitidas? Satisfacer las LI consiste en encontrar la ubicacion, forma e intensidad adecuada de las fuentes luminosas. Este tipo de problemas se define como un problema inverso de iluminacion (ILP) que se resuelve con tecnicas de optimizacion. En el contexto anterior, el objetivo de esta tesis consiste en proponer metodos eficientes para resolver ILP. Este objetivo es motivado por la brecha percibida entre los problemas habituales de diseño de iluminacion y las herramientas computacionales existentes para su resolucion. Las herramientas desarrolladas por la industria se especializan en evaluar configuraciones de iluminacion previamente diseñadas, y las desarrolladas por la academia resuelven problemas relativamente sencillos a costos elevados. Las propuestas cubren distintos aspectos del proceso de optimizacion, que van desde la formulacion del problema a su resolucion. Estan desarrolladas para el caso en que las superficies poseen reflexion e iluminacion difusas y se basan en el calculo de una aproximacion de rango bajo de la matriz de radiosidad. Algunos resultados obtenidos son: el calculo acelerado de la radiosidad de la escena en una unidad de procesamiento gr´afico (GPU); el uso de la heuristica \201Cvariable neighborhood search\201D (VNS) para la resolucion de ILP; el planteo de una estructura multinivel para tratar ILP de forma escalonada; y el uso de tecnicas para optimizar la configuracion de filtros de luz. Otros resultados obtenidos se basan en la formulacion de las LI en funcion de la media y desviacion estandar de las radiosidades halladas. Se propone un metodo para generar LI que contengan esos parametros estadisticos, y otro metodo para acelerar su evaluacion. Con estos resultados se logran tiempos de respuesta interactivos. Por último, las tecnicas anteriores adolecen de una etapa de pre-cómputo relativamente costosa, por tanto se propone acelerar el calculo de la inversa de la matriz de radiosidad a partir de una muestra de factores de forma. Los métodos aquí presentados fueron publicados en seis articulos, tres de ellos en congresos internacionales y tres en revistas arbitradas.Light is a key element that influences the way we perceive and experience our environment. As such, light is an object to be modeled in the design process, as happens with the forms and materials. The lighting intentions (LI) are the objectives and constraints that designers want to achieve in the process of lighting design: which surfaces should be illuminated with natural and which with artificial light?, which surfaces should be in shadow?, which are the maximum and minimum intensities allowed? The fulfillment of the LI consists in finding the location, shape and intensity appropriate for the light sources. This problem is defined as an inverse lighting problem (ILP), solved by optimization techniques. In the above context, the aim of this thesis is the proposal of efficient methods to solve ILP. This objective is motivated by the perceived gap between the usual problems of lighting design, and the computational tools developed for its resolution. The tools developed by the industry specialize in evaluating previously designed lighting configurations, and those developed by the academia solve relatively simple problems at a high computational cost. The proposals cover several aspects of the optimization process, ranging from the formulation of the problem to its resolution. They are developed for the case in which the surfaces have Lambertian reflection and illumination, and are based on the calculation of a low rank approximation to the radiosity matrix. Some results are: rapid calculation of radiosity of the scene in a graphics processing unit (GPU), the use of heuristics “variable neighborhood search” (VNS) for solving ILP, the proposition of a multilevel structure to solve ILP in a stepwise approach, and the use of these techniques to optimize the configuration of light filters. Other results are based on the formulation of LI that use the mean and standard deviation of the radiosity values found. A method is proposed for generating LI containing these parameters, and another method is developed to speed up their evaluations. With these results we achieve interactive response times. Finally, the above techniques suffer from a costly pre-computing stage and therefore, a method is proposed to accelerate the calculation of the radiosity inverse matrix based on a sample of the form factors. The methods presented here were published in six articles, three of them at international conferences and three in peer reviewed journals

Resolución de problemas inversos de iluminación considerando datos fotométricos

En el diseñno arquitectónico, a los objetivos estéticos y de confort visual se han agregado aquellos relacionados con la eficiencia energética y el cumplimiento de estándares de calidad en la iluminación. Este cambio de paradigmas hace relevante el estudio y desarrollo de técnicas computacionales que ayuden en la búsqueda de buenas configuraciones de luminarias. Considerar estos nuevos objetivos durante el proceso de diseño sin las herramientas adecuadas resulta ineficiente, porque está basado principalmente en el proceso de prueba y error y en la experiencia del diseñador. Las herramientas de CAD existentes generan resultados a partir de configuraciones proporcionadas por el diseñador, sin brindar nuevas soluciones. Debido a esto surge la necesidad de otro tipo de herramienta que se base en las intenciones del diseñador para generar configuraciones de luminarias adecuadas. Esta tesis se centra en el desarrollo de nuevas y eficientes heurísticas que tengan en cuenta las propiedades fotométricas de luminarias reales, as como su ubicación y orientación. Las intenciones de iluminación (LI) del diseñador se definen como objetivos y restricciones a satisfacer, y son tratadas como problemas de optimización denominados problemas inversos de iluminación (ILP). Las configuraciones obtenidas son el punto de partida para el diseñador, dado que podrá modificarlas para contemplar otros aspectos más difíciles de modelar matemáticamente. Desde el punto de vista de los métodos de iluminación global, las técnicas propuestas utilizan la ecuación de radiosidad. Se simula la emisión de la luz de las luminarias y a través de la ecuación de radiosidad se calcula cuánta luz llega a cada parche de la escena. Se realizaron experimentos centrados en la mejora de la iluminación en el edificio Palacio de los Tribunales (Poder Judicial), donde se comparan los resultados obtenidos con aquellos propuestos por diseñadores y se muestra que las heurísticas desarrolladas tienen el potencial de facilitar el proceso de diseño de iluminación. Un análisis general muestra que las técnicas implementadas son capaces de obtener buenas soluciones en el conjunto de problemas estudiado, y de obtener tiempos de ejecución adecuados para este tipo de problemas. Por tanto, estas técnicas podrán ser utilizadas como herramientas de apoyo al proceso de diseño arquitectónico

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Challenges and New Trends in Power Electronic Devices Reliability

The rapid increase in new power electronic devices and converters for electric transportation and smart grid technologies requires a deepanalysis of their component performances, considering all of the different environmental scenarios, overload conditions, and high stressoperations. Therefore, evaluation of the reliability and availability of these devices becomes fundamental both from technical and economicalpoints of view. The rapid evolution of technologies and the high reliability level offered by these components have shown that estimating reliability through the traditional approaches is difficult, as historical failure data and/or past observed scenarios demonstrate. With the aim topropose new approaches for the evaluation of reliability, in this book, eleven innovative contributions are collected, all focusedon the reliability assessment of power electronic devices and related components

AN INTELLIGENT NAVIGATION SYSTEM FOR AN AUTONOMOUS UNDERWATER VEHICLE

The work in this thesis concerns with the development of a novel multisensor data fusion (MSDF) technique, which combines synergistically Kalman filtering, fuzzy logic and genetic algorithm approaches, aimed to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system, formed by an integration of global positioning system and inertial navigation system (GPS/INS). The Kalman filter has been a popular method for integrating the data produced by the GPS and INS to provide optimal estimates of AUVs position and attitude. In this thesis, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the later where integration with GPS data takes place. The use of an adaptation scheme based on fuzzy logic approaches to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for the adaptation scheme is first carried out using a heuristic approach. Single objective and multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation. The proposed technique is known to be the first method used in relation to AUV navigation technology and is thus considered as a major contribution thereof.J&S Marine Ltd., Qinetiq, Subsea 7 and South West Water PL

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Learning to Predict Dense Correspondences for 6D Pose Estimation

Object pose estimation is an important problem in computer vision with applications in robotics, augmented reality and many other areas. An established strategy for object pose estimation consists of, firstly, finding correspondences between the image and the object’s reference frame, and, secondly, estimating the pose from outlier-free correspondences using Random Sample Consensus (RANSAC). The first step, namely finding correspondences, is difficult because object appearance varies depending on perspective, lighting and many other factors. Traditionally, correspondences have been established using handcrafted methods like sparse feature pipelines. In this thesis, we introduce a dense correspondence representation for objects, called object coordinates, which can be learned. By learning object coordinates, our pose estimation pipeline adapts to various aspects of the task at hand. It works well for diverse object types, from small objects to entire rooms, varying object attributes, like textured or texture-less objects, and different input modalities, like RGB-D or RGB images. The concept of object coordinates allows us to easily model and exploit uncertainty as part of the pipeline such that even repeating structures or areas with little texture can contribute to a good solution. Although we can train object coordinate predictors independent of the full pipeline and achieve good results, training the pipeline in an end-to-end fashion is desirable. It enables the object coordinate predictor to adapt its output to the specificities of following steps in the pose estimation pipeline. Unfortunately, the RANSAC component of the pipeline is non-differentiable which prohibits end-to-end training. Adopting techniques from reinforcement learning, we introduce Differentiable Sample Consensus (DSAC), a formulation of RANSAC which allows us to train the pose estimation pipeline in an end-to-end fashion by minimizing the expectation of the final pose error

Reliable Navigation for SUAS in Complex Indoor Environments

Indoor environments are a particular challenge for Unmanned Aerial Vehicles (UAVs). Effective navigation through these GPS-denied environments require alternative localization systems, as well as methods of sensing and avoiding obstacles while remaining on-task. Additionally, the relatively small clearances and human presence characteristic of indoor spaces necessitates a higher level of precision and adaptability than is common in traditional UAV flight planning and execution. This research blends the optimization of individual technologies, such as state estimation and environmental sensing, with system integration and high-level operational planning. The combination of AprilTag visual markers, multi-camera Visual Odometry, and IMU data can be used to create a robust state estimator that describes position, velocity, and rotation of a multicopter within an indoor environment. However these data sources have unique, nonlinear characteristics that should be understood to effectively plan for their usage in an automated environment. The research described herein begins by analyzing the unique characteristics of these data streams in order to create a highly-accurate, fault-tolerant state estimator. Upon this foundation, the system built, tested, and described herein uses Visual Markers as navigation anchors, visual odometry for motion estimation and control, and then uses depth sensors to maintain an up-to-date map of the UAV\u27s immediate surroundings. It develops and continually refines navigable routes through a novel combination of pre-defined and sensory environmental data. Emphasis is put on the real-world development and testing of the system, through discussion of computational resource management and risk reduction