Simultaneous localization and mapping (SLAM) and Structure from Motion (SfM) are important problems in robotics and computer vision. One of the challenges is to solve a large-scale optimization problem associated with all of the robot poses, camera parameters, landmarks and measurements. Yet neither of the two reigning paradigms, direct and iterative methods, scales well to very large and complex problems. Recently, the subgraph-preconditioned conjugate gradient method has been proposed to combine the advantages of direct and iterative methods. However, how to find a good subgraph is still an open problem. The goal of this dissertation is to address the following two questions: (1) What are good subgraph preconditioners for SLAM and SfM? (2) How to find them? To this end, I introduce support theory and support graph theory to evaluate and design subgraph preconditioners for SLAM and SfM. More specifically, I make the following contributions: First, I develop graphical and probabilistic interpretations of support theory and used them to visualize the quality of subgraph preconditioners. Second, I derive a novel support-theoretic metric for the quality of spanning tree preconditioners and design an MCMC-based algorithm to find high-quality subgraph preconditioners. I further improve the efficiency of finding good subgraph preconditioners by using heuristics and domain knowledge available in the problems. Our results show that the support-theoretic subgraph preconditioners significantly improve the efficiency of solving large SLAM problems. Third, I propose a novel Hessian factor graph representation, and use it to develop a new class of preconditioners, generalized subgraph preconditioners, that combine the advantages of subgraph preconditioners and Hessian-based preconditioners. I apply them to solve large SfM problems and obtain promising results. Fourth, I develop the incremental subgraph-preconditioned conjugate gradient method for large-scale online SLAM problems. The main idea is to combine the advantages of two state-of-the-art methods, incremental smoothing and mapping, and the subgraph-preconditioned conjugate gradient method. I also show that the new method is efficient, optimal and consistent. To sum up, preconditioning can significantly improve the efficiency of solving large-scale SLAM and SfM problems. While existing preconditioning techniques do not utilize the problem structure and have no performance guarantee, I take the first step toward a more general setting and have promising results.

Micro Umanned Aerial Vehicle~(UAV) and Umanned Ground Vehicle~(UGV) teams present tremendous opportunities in expanding the range of operations for these vehicles. An effective coordination of these vehicles can take advantage of the strengths of both, while mediate each other's weaknesses. In particular, a micro UAV typically has limited flight time due to its weak payload capacity. To take advantage of the mobility and sensor coverage of a micro UAV in long range, long duration surveillance mission, a UGV can act as a mobile station for recharging or battery swap, and the ability to perform autonomous docking is a prerequisite for such operations. This work presents an approach to coordinate an autonomous docking between a quadrotor UAV and a skid-steered UGV. A joint controller is designed to eliminate the relative position error between the vehicles. The controller is validated in simulations and successful landing is achieved in indoor environment, as well as outdoor settings with standard sensors and real disturbances. Another goal for this work is to improve the autonomy of UAV-UGV teams in positioning denied environments, a very common scenarios for many robotics applications. In such environments, Simultaneous Mapping and Localization~(SLAM) capability is the foundation for all autonomous operations. A successful SLAM algorithm generates maps for path planning and object recognition, while providing localization information for position tracking. This work proposes an SLAM algorithm that is capable of generating high fidelity surface model of the surrounding, while accurately estimating the camera pose in real-time. This algorithm improves on a clear deficiency of its predecessor in its ability to perform dense reconstruction without strict volume limitation, enabling practical deployment of this algorithm on robotic systems.

Aquesta tesi tracta sobre el problema de la navegació per a vehicles submarins autònoms que operen en entorns artificials estructurats com ara ports, canals, plataformes marines i altres escenaris similars. A partir d'una estimació precisa de la posició en aquests entorns, les capacitats dels vehicles submarins s'incrementen notablement i s'obre una porta al seu funcionament autònom. El manteniment, inspecció i vigilància d'instal lacions marines són alguns exemples de possibles aplicacions. Les principals contribucions d'aquesta tesi consisteixen per una banda en el desenvolupament de diferents sistemes de localització per a aquelles situacions on es disposa d'un mapa previ de l'entorn i per l'altra en el desenvolupament d'una nova solució al problema de la Localització i Construcció Simultània de Mapes (SLAM en les seves sigles en anglès), la finalitat del qual és fer que un vehicle autònom creï un mapa de l'entorn desconegut que el rodeja i, al mateix temps, utilitzi aquest mapa per a determinar la seva pròpia posició. S'ha escollit un sonar d'imatges d'escaneig mecànic com a sensor principal per a aquest treball tant pel seu relatiu baix cost com per la seva capacitat per produir una representació detallada de l'entorn. Per altra banda, les particularitats de la seva operació i, especialment, la baixa freqúència a la que es produeixen les mesures, constitueixen els principals inconvenients que s'han hagut d'abordar en les estratègies de localització proposades. Les solucions adoptades per aquests problemes constitueixen una altra contribució d'aquesta tesi. El desenvolupament de vehicles autònoms i el seu ús com a plataformes experimentals és un altre aspecte important d'aquest treball. Experiments portats a terme tant en el laboratori com en escenaris reals d'aplicació han proporcionat les dades necessàries per a provar i avaluar els diferents sistemes de localització proposats. / This thesis is concerned with the navigation problem for autonomous underwater vehicles operating in artificial structured environments like harbours, marinas, marine platforms and other similar scenarios. Obtaining an accurate position in such scenarios would notably increase the capabilities of underwater vehicles and open the door to real autonomous operation. Maintenance, inspection and surveillance of marine installations are only a few examples of possible applications. The principal contributions of this thesis consist of the development of different localization systems for those situations in which an a priori map of the environment is available but, in particular, in the development of a novel solution to the Simultaneous Localization and Mapping (SLAM) problem. This solution pursues the objective of providing an autonomous vehicle with the ability to build a map within an unknown environment while, at the same time, using this same map to keep track of its current position. A mechanical scanning imaging sonar has been chosen as the principal sensor for this work because of its relative low cost and its capacity to produce a rich representation of the environment. On the other hand, the particularities of its operation and, especially, its low scanning rate, have presented many challenges during the development of this proposed localization strategies. The solutions adopted to address these problems constitute another contribution in this thesis. The development of underwater vehicles and their use as experimental platforms is another important aspect of the research work presented here. Experiments carried out in both laboratory and real application environments have provided the different datasets necessary for the testing and evaluation of the different localization approaches.

Thesis (M.S. in Management)--Naval Postgraduate School, June 1990. / Thesis Advisor(s): McMasters, Alan W. ; Bailey, Michael P. "June 1990." Description based on signature page as viewed on October 21, 2009. DTIC Identifier(s): Computerized simulation, logistics management. Author(s) subject terms: Multi-echelon, simulation, SLAM II, models. Includes bibliographical references (p. 142-147). Also available in print.

My dissertation focuses on the forms of costume outside the theatre. For its purpose I propose reflecting the western society at the beginning of the second decade of the 21st century as a form of staged (shaped by the abundance of dramatic narrative in media) reality. I discuss the role of mass media in relation to the spectator, e.g. visual and narrative codes effecting our own self representation.

Nos últimos anos, observamos o alvorecer de uma grande quantidade de aplicações que utilizam robôs autônomos. Para que um robô seja considerado verdadeiramente autônomo, é primordial que ele possua a capacidade de aprender sobre o ambiente no qual opera. Métodos de SLAM (Localização e Mapeamento Simultâneos) constroem um mapa do ambiente por onde o robô trafega ao mesmo tempo em que estimam a trajetória correta do robô. No entanto, para obter um mapa completo do ambiente de forma autônoma é preciso guiar o robô por todo o ambiente, o que é feito no problema de exploração. Câmeras são sensores baratos que podem ser utilizadas para a construção de mapas 3D. Porém, o problema de exploração em mapas gerados por métodos de SLAM monocular, i.e. que extraem informações de uma única câmera, ainda é um problema em aberto, pois tais métodos geram mapas esparsos ou semi-densos, que são inadequados para navegação e exploração. Para tal situação, é necessário desenvolver métodos de exploração capazes de lidar com a limitação das câmeras e com a falta de informação nos mapas gerados por SLAMs monoculares. Propõe-se uma estratégia de exploração que utilize mapas volumétricos locais, gerados através das linhas de visão, permitindo que o robô navegue em segurança. Nestes mapas locais, são definidos objetivos que levem o robô a explorar o ambiente desviando de obstáculos. A abordagem proposta visa responder a questão fundamental em exploração: "Para onde ir?". Além disso, busca determinar corretamente quando o ambiente está suficientemente explorado e a exploração deve parar. A abordagem proposta é avaliada através de experimentos em um ambiente simples (i.e. apenas uma sala) e em um ambiente compostos por diversas salas. / In recent years, we have seen the dawn of a large number of applications that use autonomous robots. For a robot to be considered truly autonomous, it is primordial that it has the ability to learn about the environment in which it operates. SLAM (Simultaneous Location and Mapping) methods build a map of the environment while estimating the robot’s correct trajectory. However, to autonomously obtain a complete map of the environment, it is necessary to guide the robot throughout the environment, which is done in the exploration problem. Cameras are inexpensive sensors that can be used for building 3D maps. However, the exploration problem in maps generated by monocular SLAM methods (i.e. that extract information from a single camera) is still an open problem, since such methods generate sparse or semi-dense maps that are ill-suitable for navigation and exploration. For such a situation, it is necessary to develop exploration methods capable of dealing with the limitation of the cameras and the lack of information in the maps generated by monocular SLAMs. We proposes an exploration strategy that uses local volumetric maps, generated using the lines of sight, allowing the robot to safely navigate. In these local maps, objectives are defined to lead the robot to explore the environment while avoiding obstacles. The proposed approach aims to answer the fundamental question in exploration: "Where to go?". In addition, it seeks to determine correctly when the environment is sufficiently explored and the exploration must stop. The effectiveness of the proposed approach is evaluated in experiments on single and multi-room environments.

abstract: To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design, control objectives for rear-wheel drive ground vehicles. Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform that can be used for conducting FAME research. A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities. The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability of commercially available vehicles costing over $2000. All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following summarizes the key hardware demonstrations presented and analyzed: (1)Cruise (v, ) control along a line, (2) Cruise (v, ) control along a curve, (3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle, (4) Finish the track with camera pan tilt structure in minimum time, (5) Finish the track without camera pan tilt structure in minimum time, (6) Vision based tracking performance with different cruise speed vx, (7) Vision based tracking performance with different camera fixed look-ahead distance L, (8) Vision based tracking performance with different delay Td from vision subsystem, (9) Manually remote controlled robot to perform indoor SLAM, (10) Autonomously line guided robot to perform indoor SLAM. For most cases, hardware data is compared with, and corroborated by, model based simulation data. In short, the thesis uses low-cost self-designed rear-wheel drive robot to demonstrate many capabilities that are critical in order to reach the longer-term FAME goal. / Dissertation/Thesis / Defense Presentation / Masters Thesis Electrical Engineering 2016

Nos últimos anos, observamos o alvorecer de uma grande quantidade de aplicações que utilizam robôs autônomos. Para que um robô seja considerado verdadeiramente autônomo, é primordial que ele possua a capacidade de aprender sobre o ambiente no qual opera. Métodos de SLAM (Localização e Mapeamento Simultâneos) constroem um mapa do ambiente por onde o robô trafega ao mesmo tempo em que estimam a trajetória correta do robô. No entanto, para obter um mapa completo do ambiente de forma autônoma é preciso guiar o robô por todo o ambiente, o que é feito no problema de exploração. Câmeras são sensores baratos que podem ser utilizadas para a construção de mapas 3D. Porém, o problema de exploração em mapas gerados por métodos de SLAM monocular, i.e. que extraem informações de uma única câmera, ainda é um problema em aberto, pois tais métodos geram mapas esparsos ou semi-densos, que são inadequados para navegação e exploração. Para tal situação, é necessário desenvolver métodos de exploração capazes de lidar com a limitação das câmeras e com a falta de informação nos mapas gerados por SLAMs monoculares. Propõe-se uma estratégia de exploração que utilize mapas volumétricos locais, gerados através das linhas de visão, permitindo que o robô navegue em segurança. Nestes mapas locais, são definidos objetivos que levem o robô a explorar o ambiente desviando de obstáculos. A abordagem proposta visa responder a questão fundamental em exploração: "Para onde ir?". Além disso, busca determinar corretamente quando o ambiente está suficientemente explorado e a exploração deve parar. A abordagem proposta é avaliada através de experimentos em um ambiente simples (i.e. apenas uma sala) e em um ambiente compostos por diversas salas. / In recent years, we have seen the dawn of a large number of applications that use autonomous robots. For a robot to be considered truly autonomous, it is primordial that it has the ability to learn about the environment in which it operates. SLAM (Simultaneous Location and Mapping) methods build a map of the environment while estimating the robot’s correct trajectory. However, to autonomously obtain a complete map of the environment, it is necessary to guide the robot throughout the environment, which is done in the exploration problem. Cameras are inexpensive sensors that can be used for building 3D maps. However, the exploration problem in maps generated by monocular SLAM methods (i.e. that extract information from a single camera) is still an open problem, since such methods generate sparse or semi-dense maps that are ill-suitable for navigation and exploration. For such a situation, it is necessary to develop exploration methods capable of dealing with the limitation of the cameras and the lack of information in the maps generated by monocular SLAMs. We proposes an exploration strategy that uses local volumetric maps, generated using the lines of sight, allowing the robot to safely navigate. In these local maps, objectives are defined to lead the robot to explore the environment while avoiding obstacles. The proposed approach aims to answer the fundamental question in exploration: "Where to go?". In addition, it seeks to determine correctly when the environment is sufficiently explored and the exploration must stop. The effectiveness of the proposed approach is evaluated in experiments on single and multi-room environments.

CoordenaÃÃo de AperfeiÃoamento de NÃvel Superior / Introduction: Visceral leishmaniasis is a serious public health problem in several parts of the developing world. In leishmaniasis, protection and healing correlate with the development of Th1 immune response, and IFN-γ is considered a key molecule in this response. In contrast, the Th2 response results in disease progression. It is noteworthy that after contact with Leishmania the production of IFN-γ differs between healthy individuals. Some of them are high IFN-γ producer (HIFN-γP), while others respond with low IFN-γ producer (LIFN-γP). This has been observed in the in vitro response to several species of Leishmania, as well as in the early phase of the disease in patients with leishmaniasis. The activation of T lymphocytes can be modulated by immunomodulatory receptors present on the surface of lymphocytes and antigen presenting cells (APCs). Signaling lymphocytic activation molecule (SLAM, CD150) is a transmembrane protein that promotes Th2 differentiation. Evidences support the involvement of this molecule in the immune response against parasites. Objective: To evaluate the role of SLAM signaling pathway in the modulation of immune response of peripheral blood mononuclear cells (PBMC) from individuals low IFN-γ producers (LP) against Leishmania chagasi. Methodology: Healthy individualsâ PBMC were stimulated in vitro with L. chagasi and monoclonal anti-SLAM (α-SLAM), in the presence or not of inflammatory cytokines (rIFN-γ or rIL-12). The supernatants of the cultures were analyzed by ELISA for the determination of IFN-γ and interleukin 10 (IL-10) concentrations. Results: Upon stimulation of PBMC with L. chagasi, the blocking of the SLAM signaling pathway with α-SLAM did not affect the synthesis of IFN-γ and IL-10, regardless of treatment with rIL-12. However, after rIFN-γ treatment of antigen stimulated cells it occurred a download of IL-10 synthesis and upload IFN-γ secretion, regardless of the blockade of SLAM signaling pathway. Conclusions: The blocking of SLAM signaling pathway with α-SLAM at the concentration of 10μg/mL does not interfere significantly in the IFN-γ and IL-10 production of PBMC from individuals LP stimulated with Leishmania chagasi promastigotes. Treatment of PBMC with rIFN-γ is able to induce a reduction of IL-10 and an increment of IFN-γ in the supernates cultures, whereas treatment with rIL-12 enhanced IFN-γ production, but does not interfered with IL-10. It is necessary to make further studies to better understand the role of the SLAM signaling pathway in the immune response of LP against Leishmania chagasi. / IntroduÃÃo: Nas leishmanioses, proteÃÃo e cura se correlacionam com o desenvolvimento de resposta imune tipo Th1, e IFN-γ à considerada uma molÃcula chave nesta resposta. Ao contrÃrio, a resposta Th2, resulta na progressÃo da doenÃa. Vale ressaltar que a produÃÃo de IFN-γ, apÃs o contato com a Leishmania, difere entre indivÃduos sadios, alguns apresentam alta produÃÃo de IFN-γ (AP), enquanto outros respondem com baixa produÃÃo (BP). Isto tem sido observado com diversas espÃcies de Leishmania, assim como na infecÃÃo natural, na fase inicial da doenÃa. A ativaÃÃo do linfÃcito T pode ser modulada por coestimuladores, presentes na superfÃcie de linfÃcitos e nas APCs, a exemplo da molÃcula sinalizadora na ativaÃÃo de linfÃcitos T (SLAM). EvidÃncias apontam para o envolvimento dessas molÃculas na regulaÃÃo da resposta imunolÃgica. Objetivo: Avaliar o papel da via de sinalizaÃÃo de SLAM na modulaÃÃo da resposta imune em cÃlulas mononucleares de sangue perifÃrico (CMSP) de indivÃduos BP, frente à estimulaÃÃo in vitro por Leishmania chagasi. Metodologia: CMSP de indivÃduos sadios BP foram estimuladas in vitro com L. chagasi na ausÃncia ou na presenÃa do anticorpo monoclonal anti-SLAM (α-SLAM), com ou sem tratamento por citocinas proinflamatÃrias (rIFN-γ ou rIL-12). Os sobrenadantes das culturas foram analisados por ELISA para IFN-γ e interleucina (IL)-10. Resultados: Sob estimulaÃÃo de L. chagasi, o bloqueio da via SLAM nÃo modificou a sÃntese de IFN-γ e IL-10, independente do tratamento com rIL-12. No entanto, o tratamento com rIFN-γ reduziu a sÃntese de IL-10 e elevou a secreÃÃo de IFN-γ endÃgeno, independente do bloqueio da via SLAM. ConclusÃes: O bloqueio da via SLAM, com adiÃÃo de α-SLAM (10Âg/mL), nÃo interfere significativamente na produÃÃo de IFN-γ e IL-10. O tratamento das CMSP com rIFN-γ à capaz de induzir a reduÃÃo da produÃÃo de IL-10 e o aumento de IFN-γ de forma significativa, enquanto que o tratamento com rIL-12 aumenta a produÃÃo de IFN-γ, mas nÃo interfere na produÃÃo de IL-10 dos indivÃduos baixos produtores. Faz-se necessÃrio ampliar o estudo da aÃÃo imunomoduladora da SLAM frente à Leishmania chagasi, para um melhor entendimento do papel desta via de sinalizaÃÃo na resposta imunolÃgica dos BP.

En los métodos actuales de SLAM visual, los mapas son representados mediante landmarks puntuales. Como la observación de un landmark puntual entrega sólo información angular sobre la cámara, se debe estimar una matriz de covarianza que considere todos los puntos para poder converger a una escala global. Sin embargo, como la complejidad computacional asociada al trabajo con matrices de covarianza crece de una forma cuadrática respecto al número de landmarks, la cantidad máxima de puntos con los cuales se puede trabajar n tiempo real está limitada a unos cientos. En este trabajo se propone un sistema de SLAM visual basado en el uso de los denominados landmarks cuerpo rígido. Un landmark cuerpo rígido representa la pose completa 6D (posición y orientación) de un cuerpo rígido en el espacio, y la observación de uno de estos landmarks proporciona información completa acerca de la pose de una cámara móvil. Cada landmarks cuerpo rígido es creado a partir de un conjunto de N landmarks puntuales mediante el colapso de 3N componentes del estado en 7 nuevas componentes del estado, además de un conjunto de parámetros que describe la forma del landmark. Los landmark cuerpo rígido son representados y estimados usando puntocuaterniones, los cuales son introducidos en este trabajo. Mediante el uso de los landmarks cuerpo rígido, el tiempo de ejecución del sistema de SLAM puede reducirse hasta un 5.5% a medida que la cantidad de landmarks aumenta. El sistema SLAM propuesto es validado y simulado en secuencias de video reales. El método propuesto puede ser extendido a cualquier sistema de SLAM que se base en el uso de landmarks puntuales, incluyendo aquellos generados mediante sensores láser.