FLAT2D: Fast localization from approximate transformation into 2D

Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment

Deteção dos limites navegáveis da estrada por análise da densidade de nuvens de pontos acumulados

As part of the Atlas project, this dissertation aims to identify the navigable limits of the road by analyzing the density of accumulated point clouds, obtained through laser readings from a SICK LD-MRS sensor. This sensor, installed in front of the AtlasCar2, has the purpose of identifying obstacles at road level and from it the creation of occupation grids that delimit the navigable space of the vehicle is proposed. First, the point cloud density is converted into an occupancy density grid, normalized in each frame in relation to the maximum density. Edge detection algorithms and gradient filters are subsequently applied to the density grid, in order to detect patterns that match sudden changes in density, both positive and negative. To these grids are applied thresholds in order to remove irrelevant information. Finally, a methodology for quantitative evaluation of algorithms was also developed, using KML files to define road boundaries and, relying on the accuracy of the GPS data obtained, comparing the actual navigable space with the one obtained by the methodology for detection of road boundaries and thus evaluating the performance of the work developed. In this work, the results of the different algorithms are presented, as well as several tests taking into account the influence of grid resolution, car speed, among others. In general, the work developed meets the initially proposed objectives, being able to detect both positive and negative obstacles and being minimally robust to speed and road conditions.No âmbito do projeto Atlas, esta dissertação prevê a identificação dos limites navegáveis da estrada através da análise da densidade da acumulaçao de nuvens de pontos, obtidas através de leituras laser provenientes de um sensor SICK LD-MRS. Este sensor, instalado na frente do AtlasCar2, tem como propósito a identificação de obstáculos ao nível da estrada e a partir dos seus dados prevê-se a criação de grelhas de ocupação que delimitem o espaço navegável do veículo. Em primeiro lugar, a densidade da nuvem de pontos é transformada numa grelha de densidade normalizada em cada frame em relação à densidade máxima, à qual posteriormente são aplicados algoritmos de deteção de arestas e filtros de gradiente com o objetivo de detetar padrões que correspondam a mudanças súbitas de densidade, tanto positivas como negativas. A estas grelhas são aplicados limiares de forma a eliminar informação irrelevante. Por fim, foi desenvolvida também uma metodologia de avaliação quantitativa dos algoritmos, usando ficheiros KML para deliniar limites da estrada e, contanto com a precisão dos dados de GPS obtidos, comparar o espaço navegável real com o obtido pela metodologia de deteção de limites de estrada e assim avaliar o desempenho dos algoritmos desenvolvidos. Neste trabalho são apresentados resultados dos diferentes algoritmos, bem como diversos testes tendo em conta a influência da resolução de grelha, velocidade do carro, entre outros. O trabalho desenvovido cumpre os objetivos propostos inicialmente, sendo capaz de detetar ambos obstáculos positivos e negativos e sendo minimamente robusto a velocidade e condições de estrada.Mestrado em Engenharia Mecânic

Optimal Energy-Driven Aircraft Design Under Uncertainty

Aerodynamic shape design robust optimization is gaining popularity in the aeronautical industry as it provides optimal solutions that do not deteriorate excessively in the presence of uncertainties. Several approaches exist to quantify uncertainty and, the dissertation deals with the use of risk measures, particularly the Value at Risk (VaR) and the Conditional Value at Risk (CVaR). The calculation of these measures relies on the Empirical Cumulative Distribution Function (ECDF) construction. Estimating the ECDF with a Monte Carlo sampling can require many samples, especially if good accuracy is needed on the probability distribution tails. Furthermore, suppose the quantity of interest (QoI) requires a significant computational effort, as in this dissertation, where has to resort to Computational Fluid Dynamics (CFD) methods. In that case, it becomes imperative to introduce techniques that reduce the number of samples needed or speed up the QoI evaluations while maintaining the same accuracy. Therefore, this dissertation focuses on investigating methods for reducing the computational cost required to perform optimization under uncertainty. Here, two cooperating approaches are introduced: speeding up the CFD evaluations and approximating the statistical measures. Specifically, the CFD evaluation is sped up by employing a far-field approach, capable of providing better estimations of aerodynamic forces on coarse grids with respect to a classical near-field approach. The advantages and critical points of the implementation of this method are explored in viscous and inviscid test cases. On the other hand, the approximation of the statistical measure is performed by using the gradient-based method or a surrogate-based approach. Notably, the gradient-based method uses adjoint field solutions to reduce the time required to evaluate them through CFD drastically. Both methods are used to solve the shape optimization of the central section of a Blended Wing Body under uncertainty. Moreover, a multi-fidelity surrogate-based optimization is used for the robust design of a propeller blade. Finally, additional research work documented in this dissertation focuses on utilizing an optimization algorithm that mixes integer and continuous variables for the robust optimization of High Lift Devices

Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93532/1/21426_ftp.pd

3D mapping and path planning from range data

This thesis reports research on mapping, terrain classification and path planning. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common proprioceptive modality, that of three-dimensional laser range scanning. The ultimate goal is to deliver navigation paths for challenging mobile robotics scenarios. For this reason we also deliver safe traversable regions from a previously computed globally consistent map. We first examine the problem of registering dense point clouds acquired at different instances in time. We contribute with a novel range registration mechanism for pairs of 3D range scans using point-to-point and point-to-line correspondences in a hierarchical correspondence search strategy. For the minimization we adopt a metric that takes into account not only the distance between corresponding points, but also the orientation of their relative reference frames. We also propose FaMSA, a fast technique for multi-scan point cloud alignment that takes advantage of the asserted point correspondences during sequential scan matching, using the point match history to speed up the computation of new scan matches. To properly propagate the model of the sensor noise and the scan matching, we employ first order error propagation, and to correct the error accumulation from local data alignment, we consider the probabilistic alignment of 3D point clouds using a delayed-state Extended Information Filter (EIF). In this thesis we adapt the Pose SLAM algorithm to the case of 3D range mapping, Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where sensor data is solely used to produce relative constraints between robot poses. These dense mapping techniques are tested in several scenarios acquired with our 3D sensors, producing impressively rich 3D environment models. The computed maps are then processed to identify traversable regions and to plan navigation sequences. In this thesis we present a pair of methods to attain high-level off-line classification of traversable areas, in which training data is acquired automatically from navigation sequences. Traversable features came from the robot footprint samples during manual robot motion, allowing us to capture terrain constrains not easy to model. Using only some of the traversed areas as positive training samples, our algorithms are tested in real scenarios to find the rest of the traversable terrain, and are compared with a naive parametric and some variants of the Support Vector Machine. Later, we contribute with a path planner that guarantees reachability at a desired robot pose with significantly lower computation time than competing alternatives. To search for the best path, our planner incrementally builds a tree using the A* algorithm, it includes a hybrid cost policy to efficiently expand the search tree, combining random sampling from the continuous space of kinematically feasible motion commands with a cost to goal metric that also takes into account the vehicle nonholonomic constraints. The planer also allows for node rewiring, and to speed up node search, our method includes heuristics that penalize node expansion near obstacles, and that limit the number of explored nodes. The method book-keeps visited cells in the configuration space, and disallows node expansion at those configurations in the first full iteration of the algorithm. We validate the proposed methods with experiments in extensive real scenarios from different very complex 3D outdoors environments, and compare it with other techniques such as the A*, RRT and RRT* algorithms.Esta tesis reporta investigación sobre el mapeo, clasificación de terreno y planificación de trayectorias. Estos son problemas clásicos en robótica los cuales generalmente se estudian de forma independiente, aquí se vinculan enmarcandolos con una modalidad propioceptiva común: un láser de rango 3D. El objetivo final es ofrecer trayectorias de navegación para escenarios complejos en el marco de la robótica móvil. Por esta razón también entregamos regiones transitables en un mapa global consistente calculado previamente. Primero examinamos el problema de registro de nubes de puntos adquiridas en diferentes instancias de tiempo. Contribuimos con un novedoso mecanismo de registro de pares de imagenes de rango 3D usando correspondencias punto a punto y punto a línea, en una estrategia de búsqueda de correspondencias jerárquica. Para la minimización optamos por una metrica que considera no sólo la distancia entre puntos, sino también la orientación de los marcos de referencia relativos. También proponemos FAMSA, una técnica para el registro rápido simultaneo de multiples nubes de puntos, la cual aprovecha las correspondencias de puntos obtenidas durante el registro secuencial, usando inicialmente la historia de correspondencias para acelerar el cálculo de las correspondecias en los nuevos registros de imagenes. Para propagar adecuadamente el modelo del ruido del sensor y del registro de imagenes, empleamos la propagación de error de primer orden, y para corregir el error acumulado del registro local, consideramos la alineación probabilística de nubes de puntos 3D utilizando un Filtro Extendido de Información de estados retrasados. En esta tesis adaptamos el algóritmo Pose SLAM para el caso de mapas con imagenes de rango 3D, Pose SLAM es la variante de SLAM donde solamente se estima la trayectoria del robot, usando los datos del sensor como restricciones relativas entre las poses robot. Estas técnicas de mapeo se prueban en varios escenarios adquiridos con nuestros sensores 3D produciendo modelos 3D impresionantes. Los mapas obtenidos se procesan para identificar regiones navegables y para planificar secuencias de navegación. Presentamos un par de métodos para lograr la clasificación de zonas transitables fuera de línea. Los datos de entrenamiento se adquieren de forma automática usando secuencias de navegación obtenidas manualmente. Las características transitables se captan de las huella de la trayectoria del robot, lo cual permite capturar restricciones del terreno difíciles de modelar. Con sólo algunas de las zonas transitables como muestras de entrenamiento positivo, nuestros algoritmos se prueban en escenarios reales para encontrar el resto del terreno transitable. Los algoritmos se comparan con algunas variantes de la máquina de soporte de vectores (SVM) y una parametrizacion ingenua. También, contribuimos con un planificador de trayectorias que garantiza llegar a una posicion deseada del robot en significante menor tiempo de cálculo a otras alternativas. Para buscar el mejor camino, nuestro planificador emplea un arbol de busqueda incremental basado en el algoritmo A*. Incluimos una póliza de coste híbrido para crecer de manera eficiente el árbol, combinando el muestro aleatorio del espacio continuo de comandos cinemáticos del robot con una métrica de coste al objetivo que también concidera las cinemática del robot. El planificador además permite reconectado de nodos, y, para acelerar la búsqueda de nodos, se incluye una heurística que penaliza la expansión de nodos cerca de los obstáculos, que limita el número de nodos explorados. El método conoce las céldas que ha visitado del espacio de configuraciones, evitando la expansión de nodos en configuraciones que han sido vistadas en la primera iteración completa del algoritmo. Los métodos propuestos se validán con amplios experimentos con escenarios reales en diferentes entornos exteriores, asi como su comparación con otras técnicas como los algoritmos A*, RRT y RRT*.Postprint (published version

Enhancing scene text recognition with visual context information

This thesis addresses the problem of improving text spotting systems, which aim to detect and recognize text in unrestricted images (e.g. a street sign, an advertisement, a bus destination, etc.). The goal is to improve the performance of off-the-shelf vision systems by exploiting the semantic information derived from the image itself. The rationale is that knowing the content of the image or the visual context can help to decide which words are the correct andidate words. For example, the fact that an image shows a coffee shop makes it more likely that a word on a signboard reads as Dunkin and not unkind. We address this problem by drawing on successful developments in natural language processing and machine learning, in particular, learning to re-rank and neural networks, to present post-process frameworks that improve state-of-the-art text spotting systems without the need for costly data-driven re-training or tuning procedures. Discovering the degree of semantic relatedness of candidate words and their image context is a task related to assessing the semantic similarity between words or text fragments. However, semantic relatedness is more general than similarity (e.g. car, road, and traffic light are related but not similar) and requires certain adaptations. To meet the requirements of these broader perspectives of semantic similarity, we develop two approaches to learn the semantic related-ness of the spotted word and its environmental context: word-to-word (object) or word-to-sentence (caption). In the word-to-word approach, word embed-ding based re-rankers are developed. The re-ranker takes the words from the text spotting baseline and re-ranks them based on the visual context from the object classifier. For the second, an end-to-end neural approach is designed to drive image description (caption) at the sentence-level as well as the word-level (objects) and re-rank them based not only on the visual context but also on the co-occurrence between them. As an additional contribution, to meet the requirements of data-driven ap-proaches such as neural networks, we propose a visual context dataset for this task, in which the publicly available COCO-text dataset [Veit et al. 2016] has been extended with information about the scene (including the objects and places appearing in the image) to enable researchers to include the semantic relations between texts and scene in their Text Spotting systems, and to offer a common evaluation baseline for such approaches.Aquesta tesi aborda el problema de millorar els sistemes de reconeixement de text, que permeten detectar i reconèixer text en imatges no restringides (per exemple, un cartell al carrer, un anunci, una destinació d’autobús, etc.). L’objectiu és millorar el rendiment dels sistemes de visió existents explotant la informació semàntica derivada de la pròpia imatge. La idea principal és que conèixer el contingut de la imatge o el context visual en el que un text apareix, pot ajudar a decidir quines són les paraules correctes. Per exemple, el fet que una imatge mostri una cafeteria fa que sigui més probable que una paraula en un rètol es llegeixi com a Dunkin que no pas com unkind. Abordem aquest problema recorrent a avenços en el processament del llenguatge natural i l’aprenentatge automàtic, en particular, aprenent re-rankers i xarxes neuronals, per presentar solucions de postprocés que milloren els sistemes de l’estat de l’art de reconeixement de text, sense necessitat de costosos procediments de reentrenament o afinació que requereixin grans quantitats de dades. Descobrir el grau de relació semàntica entre les paraules candidates i el seu context d’imatge és una tasca relacionada amb l’avaluació de la semblança semàntica entre paraules o fragments de text. Tanmateix, determinar l’existència d’una relació semàntica és una tasca més general que avaluar la semblança (per exemple, cotxe, carretera i semàfor estan relacionats però no són similars) i per tant els mètodes existents requereixen certes adaptacions. Per satisfer els requisits d’aquestes perspectives més àmplies de relació semàntica, desenvolupem dos enfocaments per aprendre la relació semàntica de la paraula reconeguda i el seu context: paraula-a-paraula (amb els objectes a la imatge) o paraula-a-frase (subtítol de la imatge). En l’enfocament de paraula-a-paraula s’usen re-rankers basats en word-embeddings. El re-ranker pren les paraules proposades pel sistema base i les torna a reordenar en funció del context visual proporcionat pel classificador d’objectes. Per al segon cas, s’ha dissenyat un enfocament neuronal d’extrem a extrem per explotar la descripció de la imatge (subtítol) tant a nivell de frase com a nivell de paraula i re-ordenar les paraules candidates basant-se tant en el context visual com en les co-ocurrències amb el subtítol. Com a contribució addicional, per satisfer els requisits dels enfocs basats en dades com ara les xarxes neuronals, presentem un conjunt de dades de contextos visuals per a aquesta tasca, en el què el conjunt de dades COCO-text disponible públicament [Veit et al. 2016] s’ha ampliat amb informació sobre l’escena (inclosos els objectes i els llocs que apareixen a la imatge) per permetre als investigadors incloure les relacions semàntiques entre textos i escena als seus sistemes de reconeixement de text, i oferir una base d’avaluació comuna per a aquests enfocaments

EFFECTIVE NAVIGATION AND MAPPING OF A CLUTTERED ENVIRONMENT USING A MOBILE ROBOT

Today, the as-is three-dimensional point cloud acquisition process for understanding scenes of interest, monitoring construction progress, and detecting safety hazards uses a laser scanning system mounted on mobile robots, which enables it faster and more automated, but there is still room for improvement. The main disadvantage of data collection using laser scanners is that point cloud data is only collected in a scanner’s line of sight, so regions in three-dimensional space that are occluded by objects are not observable. To solve this problem and obtain a complete reconstruction of sites without information loss, scans must be taken from multiple viewpoints. This thesis describes how such a solution can be integrated into a fully autonomous mobile robot capable of generating a high-resolution three-dimensional point cloud of a cluttered and unknown environment without a prior map. First, the mobile platform estimates unevenness of terrain and surrounding environment. Second, it finds the occluded region in the currently built map and determines the effective next scan location. Then, it moves to that location by using grid-based path planner and unevenness estimation results. Finally, it performs the high-resolution scanning that area to fill out the point cloud map. This process repeats until the designated scan region filled up with scanned point cloud. The mobile platform also keeps scanning for navigation and obstacle avoidance purposes, calculates its relative location, and builds the surrounding map while moving and scanning, a process known as simultaneous localization and mapping. The proposed approaches and the system were tested and validated in an outdoor construction site and a simulated disaster environment with promising results.Ph.D

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions

Indiana University-Purdue University Indianapolis (IUPUI)Phenotyping definitions are essential in cohort identification when conducting clinical research, but they become an obstacle when they are not readily available. Developing new definitions manually requires expert involvement that is labor-intensive, time-consuming, and unscalable. Moreover, automated approaches rely mostly on electronic health records’ data that suffer from bias, confounding, and incompleteness. Limited efforts established in utilizing text-mining and data-driven approaches to automate extraction and literature-based knowledge discovery of phenotyping definitions and to support their scalability. In this dissertation, we proposed a text-mining pipeline combining rule-based and machine-learning methods to automate retrieval, classification, and extraction of phenotyping definitions’ information from literature. To achieve this, we first developed an annotation guideline with ten dimensions to annotate sentences with evidence of phenotyping definitions' modalities, such as phenotypes and laboratories. Two annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text observational studies’ methods sections (n=86). Percent and Kappa statistics showed high inter-annotator agreement on sentence-level annotations. Second, we constructed two validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level. We applied the abstract-level classifier on a large-scale biomedical literature of over 20 million abstracts published between 1975 and 2018 to classify positive abstracts (n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from their methods sections and used the full-text sentence-level classifier to extract positive sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the positively classified sentences. Lexica-based methods were used to recognize medical concepts in these sentences (n=19,423). Co-occurrence and association methods were used to identify and rank phenotype candidates that are associated with a phenotype of interest. We derived 12,616,465 associations from our large-scale corpus. Our literature-based associations and large-scale corpus contribute in building new data-driven phenotyping definitions and expanding existing definitions with minimal expert involvement