Semi-autonomous uncrewed aerial vehicles (UAVs) are telerobotic operations by definition where the UAV assumes the role of a telerobot and the human assumes the role of a supervisor. All telerobotic operations are susceptible to time delays due to communication, mechanical, and other constraints. Typically, these delays are small and do not affect the telerobotic operation for most of the tasks. However, for long-distance telerobotic operations like interplanetary rovers, deep underwater vehicles, etc. the delays can be so significant that they can render the entire operation void. This dissertation investigates the use of a novel heterogeneous stereo-vision system to mitigate the effects of time delays in a UAV-based visual interface presented to a human operator. The heterogeneous stereo-vision system consists of an omnidirectional camera and a pan-tilt-zoom camera. Two predictive display setups were developed that modify the delayed video imagery that would otherwise be presented to the operator in a way that provides an almost immediate visual response to the operator's control actions. The usability of the system is determined through human performance testing with and without the predictive algorithms. The results indicate that the predictive algorithm allows more efficient, accurate, and user-friendly operation. The second half of the dissertation deals with improving the performance of the predictive display and expanding the concept of the prediction from a stationary gimbal-camera system to a moving 6 DoF aircraft. Specifically, it talks about a novel extended Kalman filter (EKF)-based nonlinear predictor – the extended Kalman predictor (EKP) – and compares its performance with two linear predictors, the Smith predictor (SP) and the Kalman predictor (KP). This dissertation provides the mathematical formulation of the EKP, as well as the two linear predictors, and describes their use with simulated flight data obtained using a nonlinear motion model for a small, fixed-wing UAV. The EKP performs comparably to the KP when the aircraft motion experiences small perturbations from a nominal trajectory, but the EKP outperforms the KP for larger excursions. The SP performs poorly in every case. / Doctor of Philosophy / Semi-autonomous uncrewed aerial vehicles (UAVs) are telerobotic operations by definition where the aerial vehicle assumes the role of a telerobot and the human assumes the role of a supervisor. This dissertation addresses the challenges posed by time delays in uncrewed aerial vehicle operations, particularly for long-distance operations such as interplanetary exploration and deep-sea missions. It investigates the use of a novel heterogeneous stereo-vision system to mitigate these delays, providing operators with nearly real-time visual feedback. Human performance testing confirms the predictive algorithm allows more efficient, accurate, and user-friendly operation. Additionally, the dissertation presents advancements in the predictive display performance for moving UAVs with six degrees of freedom. It introduces a novel extended Kalman predictor and compares it to traditional linear predictors like the Smith predictor and the Kalman predictor using simulated flight data. The extended Kalman predictor demonstrates superior performance for larger deviations from trajectory, highlighting its effectiveness in predicting the motion of an aircraft when there are time delays present.

Navegação autônoma é um tópico abrangente cuja atenção por parte da comunidade de robôs móveis vemaumentando ao longo dos anos. O problema consiste em guiar um robô de forma inteligente por um determinado percurso sem ajuda humana. Esta dissertação apresenta um sistema de navegação para ambientes abertos baseado em visão estéreo. Uma câmera estéreo é utilizada na captação de imagens do ambiente e, utilizando o mapa de disparidades gerado por um método estéreo semi-global, dois métodos de detecção de obstáculos são utilizando para segmentar as imagens em regiões navegáveis e não navegáveis. Posteriormente esta classificação é utilizada em conjunto com um método de desvio de obstáculos, resultando em um sistema completo de navegação autônoma. Os resultados obtidos por está dissertação incluem a avaliação de dois métodos estéreo, esta sendo favorável ao método estéreo empregado (semi-global). Foram feitos testes visando avaliar a qualidade e custo computacional de dois métodos para detecção de obstáculos, um baseado em plano e outro baseado em cone. Tais testes deixaram claras as limitações de ambos os métodos e levaram a uma implementação paralela do método baseado em cone. Utilizando uma unidade de processamento gráfico, a versão paralelizada do método baseado em cone atingiu um ganho no tempo computacional de aproximadamente dez vezes. Por fim, os resultados demonstrarão o sistema completo em funcionamento, onde a plataforma robótica utilizada, um veículo elétrico, foi capaz de desviar de pessoas e cones alcançando seu objetivo seguramente / Autonomous navigation is a broad topic that has received increasing attention from the community of mobile robots over the years. The problem is to guide a robot in a smart way for a certain route without human help. This dissertation presents a navigation system for open environments based on stereo vision. A stereo camera is used to capture images of the environment and based on the disparity map generated by a semi-global stereo method, two obstacle detection methods are used to segment the images into navigable and non-navigable regions. Subsequently, this classification is employed in conjunction with a obstacle avoidance method, resulting in a complete autonomous navigation system. The results include an evaluation two stereo methods, this being favorable to the employed stereo method (semi-global). Tests were performed to evaluate the quality and computational cost of two methods for obstacle detection, a plane based one and a cone based. Such tests have left clear the limitations of both methods and led to a parallel implementation of the cone based method. Using a graphics processing unit, a parallel version of the cone based method reached a gain in computational time of approximately ten times. Finally, the results demonstrate the complete system in operation, where the robotic platform used, an electric vehicle, was able to dodge people and cones reaching its goal safely

A obtenção e a reconstrução da geometria tridimensional (3D) de objetos e ambientes têm importância crescente em áreas como visão computacional e computação gráfica. As formas atuais de obtenção e reconstrução 3D necessitam de equipamentos e montagens sofisticadas que, por conseqüência, têm custos elevados e aplicação limitada. Este trabalho apresenta criticamente os principais algoritmos para a reconstrução 3D a partir de par de imagens estéreo e identifica os mais viáveis para utilização com equipamentos convencionais. Por meio da implementação de alguns destes algoritmos, da comparação dos resultados obtidos em sua execução e também pela comparação com os resultados encontrados na literatura, são identificadas as principais deficiências. São propostas adequações aos algoritmos existentes, em particular, é apresentada a proposta da técnica das faixas que proporciona a redução drástica no consumo de memória para o processamento da geometria 3D e que possui desempenho computacional melhor em relação às técnicas tradicionais. Foi implementado um protótipo de sistema de reconstrução 3D que permite a reconstrução pelas diferentes técnicas estudadas e propostas, bem como permite visualizar o cenário reconstruído sob diferentes pontos de vista de forma interativa. / The acquisition and reconstruction of three-dimensional (3D) geometry of objects and environments have their importance growing in areas such as Computer Vision and Computer Graphics. The current methods to acquire and reconstruct three-dimensional data need sophisticated equipments and assemblies, which have expensive costs and limited applications. This work presents the main algorithms for 3D reconstruction using a pair of stereo images and identifies which are viable to use with conventional equipments. Through the implementation of some of these algorithms, by comparing the results obtained and comparing with the results presented in the literature, the main limitations were identified. This work proposes adjustments in the existing algorithms, in particular it proposes the stripping technique, which provides a huge memory usage reduction for 3D geometry processing and better computing performance if compared with traditional approaches. A prototype system for 3D reconstruction was implemented, which allows the reconstruction using the different researched and proposed techniques and allows interactive visualization of the reconstructed scene in different angles.

A obtenção e a reconstrução da geometria tridimensional (3D) de objetos e ambientes têm importância crescente em áreas como visão computacional e computação gráfica. As formas atuais de obtenção e reconstrução 3D necessitam de equipamentos e montagens sofisticadas que, por conseqüência, têm custos elevados e aplicação limitada. Este trabalho apresenta criticamente os principais algoritmos para a reconstrução 3D a partir de par de imagens estéreo e identifica os mais viáveis para utilização com equipamentos convencionais. Por meio da implementação de alguns destes algoritmos, da comparação dos resultados obtidos em sua execução e também pela comparação com os resultados encontrados na literatura, são identificadas as principais deficiências. São propostas adequações aos algoritmos existentes, em particular, é apresentada a proposta da técnica das faixas que proporciona a redução drástica no consumo de memória para o processamento da geometria 3D e que possui desempenho computacional melhor em relação às técnicas tradicionais. Foi implementado um protótipo de sistema de reconstrução 3D que permite a reconstrução pelas diferentes técnicas estudadas e propostas, bem como permite visualizar o cenário reconstruído sob diferentes pontos de vista de forma interativa. / The acquisition and reconstruction of three-dimensional (3D) geometry of objects and environments have their importance growing in areas such as Computer Vision and Computer Graphics. The current methods to acquire and reconstruct three-dimensional data need sophisticated equipments and assemblies, which have expensive costs and limited applications. This work presents the main algorithms for 3D reconstruction using a pair of stereo images and identifies which are viable to use with conventional equipments. Through the implementation of some of these algorithms, by comparing the results obtained and comparing with the results presented in the literature, the main limitations were identified. This work proposes adjustments in the existing algorithms, in particular it proposes the stripping technique, which provides a huge memory usage reduction for 3D geometry processing and better computing performance if compared with traditional approaches. A prototype system for 3D reconstruction was implemented, which allows the reconstruction using the different researched and proposed techniques and allows interactive visualization of the reconstructed scene in different angles.

Navegação autônoma é um tópico abrangente cuja atenção por parte da comunidade de robôs móveis vemaumentando ao longo dos anos. O problema consiste em guiar um robô de forma inteligente por um determinado percurso sem ajuda humana. Esta dissertação apresenta um sistema de navegação para ambientes abertos baseado em visão estéreo. Uma câmera estéreo é utilizada na captação de imagens do ambiente e, utilizando o mapa de disparidades gerado por um método estéreo semi-global, dois métodos de detecção de obstáculos são utilizando para segmentar as imagens em regiões navegáveis e não navegáveis. Posteriormente esta classificação é utilizada em conjunto com um método de desvio de obstáculos, resultando em um sistema completo de navegação autônoma. Os resultados obtidos por está dissertação incluem a avaliação de dois métodos estéreo, esta sendo favorável ao método estéreo empregado (semi-global). Foram feitos testes visando avaliar a qualidade e custo computacional de dois métodos para detecção de obstáculos, um baseado em plano e outro baseado em cone. Tais testes deixaram claras as limitações de ambos os métodos e levaram a uma implementação paralela do método baseado em cone. Utilizando uma unidade de processamento gráfico, a versão paralelizada do método baseado em cone atingiu um ganho no tempo computacional de aproximadamente dez vezes. Por fim, os resultados demonstrarão o sistema completo em funcionamento, onde a plataforma robótica utilizada, um veículo elétrico, foi capaz de desviar de pessoas e cones alcançando seu objetivo seguramente / Autonomous navigation is a broad topic that has received increasing attention from the community of mobile robots over the years. The problem is to guide a robot in a smart way for a certain route without human help. This dissertation presents a navigation system for open environments based on stereo vision. A stereo camera is used to capture images of the environment and based on the disparity map generated by a semi-global stereo method, two obstacle detection methods are used to segment the images into navigable and non-navigable regions. Subsequently, this classification is employed in conjunction with a obstacle avoidance method, resulting in a complete autonomous navigation system. The results include an evaluation two stereo methods, this being favorable to the employed stereo method (semi-global). Tests were performed to evaluate the quality and computational cost of two methods for obstacle detection, a plane based one and a cone based. Such tests have left clear the limitations of both methods and led to a parallel implementation of the cone based method. Using a graphics processing unit, a parallel version of the cone based method reached a gain in computational time of approximately ten times. Finally, the results demonstrate the complete system in operation, where the robotic platform used, an electric vehicle, was able to dodge people and cones reaching its goal safely

In the industrial area, robots are an important part of the technological resources available to perform manipulation tasks in manufacturing, assembly, the transportation of dangerous waste, and a variety of applications. Specialized systems of computer vision have entered the market to solve problems that other technologies have been unable to address. This document analyzes a stereo vision system that is used to provide the center of mass of an object in three dimensions. This kind of application is mounted using two or more cameras that are aligned along the same axis and give the possibility to measure the depth of a point in the space. The stereoscopic system described, measures the position of an object using a combination between the 2D recognition, which implies the calculus of the coordinates of the center of mass and using moments, and the disparity that is found comparing two images: one of the right and one of the left. This converts the system into a 3D reality viewfinder, emulating the human eyes, which are capable of distinguishing depth with good precision.The proposed stereo vision system is integrated into a 5 degree of freedom pneumatic robot, which can be programmed using the GRAFCET method by means of commercial software. The cameras are mounted in the lateral plane of the robot to ensure that all the pieces in the robot's work area can be observed.For the implementation, an algorithm is developed for recognition and position measurement using open sources in C++. This ensures that the system can remain as open as possible once it is integrated with the robot. The validation of the work is accomplished by taking samples of the objects to be manipulated and generating robot's trajectories to see if the object can be manipulated by its end effector or not. The results show that is possible to manipulate pieces in a visually crowded space with acceptable precision. However, the precision reached does not allow the robot to perform tasks that require higher accuracy as the one is needed in manufacturing assembly process of little pieces or in welding applications. / En el área industrial los robots forman parte importante del recurso tecnológico disponible para tareas de manipulación en manufactura, ensamble, manejo de residuos peligrosos y aplicaciones varias. Los sistemas de visión computacional se han ingresado al mercado como soluciones a problemas que otros tipos de sensores y métodos no han podido solucionar. El presente trabajo analiza un sistema de visión estereoscópico aplicado a un robot. Este arreglo permite la medición de coordenadas del centro de un objeto en las tres dimensiones, de modo que, le da al robot la posibilidad de trabajar en el espacio y no solo en un plano. El sistema estereoscópico consiste en el uso de dos o más cámaras alineadas en alguno de sus ejes, mediante las cuales, es posible calcular la profundidad a la que se encuentran los objetos. En el presente, se mide la posición de un objeto haciendo una combinación entre el reconocimiento 2D y la medición de las coordenadas y de su centro calculadas usando momentos. En el sistema estereoscópico, se añade la medición de la última coordenada mediante el cálculo de la disparidad encontrada entre las imágenes de las cámaras inalámbricas izquierda y derecha, que convierte al sistema en un visor 3D de la realidad, emulando los ojos humanos capaces de distinguir profundidades con cierta precisión. El sistema de visión computacional propuesto es integrado a un robot neumático de 5 grados de libertad el cual puede ser programado desde la metodología GRAFCET mediante software de uso comercial. Las cámaras del sistema de visión están montadas en el plano lateral del robot de modo tal, que es posible visualizar las piezas que quedan dentro de su volumen de trabajo. En la implementación, se desarrolla un algoritmo de reconocimiento y medición de posición, haciendo uso de software libre en lenguaje C++. De modo que, en la integración con el robot, el sistema pueda ser lo más abierto posible. La validación del trabajo se logra tomando muestras de los objetos a ser manipulados y generando trayectorias para el robot, a fin de visualizar si la pieza pudo ser captada por su garra neumática o no. Los resultados muestran que es posible lograr la manipulación de piezas en un ambiente visualmente cargado y con una precisión aceptable. Sin embargo, se observa que la precisión no permite que el sistema pueda ser usado en aplicaciones donde se requiere precisión al nivel de los procesos de ensamblado de piezas pequeñas o de soldadura.

Curbs and road markings were designed to provide a visual low-level spatial perception of road environments. In this sense, a perception system capable of detecting those road features is of utmost importance for an autonomous vehicle. In vision-based approaches, few works have been developed for curb detection, and most of the advances on road marking detection have aimed lane markings only. Therefore, to detect all these road features, multiple algorithms running simultaneously would be necessary. Alternatively, as the main contribution of this work, it was proposed to employ an architecture of Fully Convolutional Neural Network (FCNN), denominated as 3CSeg-Multinet, to detect curbs and road markings in a single inference. Since there was no labeled dataset available for training and validation, a new one was generated with Brazilian urban scenes, and they were manually labeled. By visually analyzing experimental results, the proposed approach has shown to be effective and robust against most of the clutter present on images, running at around 10 fps in a Graphics Processing Unit (GPU). Moreover, with the intention of granting spatial perception, stereo vision techniques were used to project the detected road features in a point cloud. Finally, as a way to validate the applicability of the proposed perception system on a vehicle, it was also introduced a vision-based metric localization model for the urban scenario. In an experiment, compared to the ground truth, this localization method has revealed consistency on its pose estimations in a map generated by LIDAR. / Guias e sinalizações horizontais foram projetados para fornecer a percepção visual de baixo nível do espaço das vias urbanas. Deste modo, seria de extrema importância para um veículo autônomo ter um sistema de percepção capaz de detectar tais características visuais. Em abordagens baseadas em visão, poucos trabalhos foram desenvolvidos para detecção de guias, e a maioria dos avanços em detecção de sinalizações horizontais foi focada na detecção de faixas apenas. Portanto, para que fosse possível detectar todas essas características visuais, seria necessário executar diversos algoritmos simultaneamente. Alternativamente, como sendo a principal contribuição deste trabalho, foi proposto a adoção de uma Rede Neural Totalmente Convolutiva, denominado 3CSeg-Multinet, para detectar guias e sinalizações horizontais em apenas uma inferência. Como não havia um conjunto de dados rotulados disponível para treinar e validar a rede, foi gerado um novo conjunto com imagens capturadas em ambiente urbano brasileiro, e foi realizado a rotulação manual. Através de uma análise visual dos resultados experimentais obtidos, o método proposto mostrou-se eficaz e robusto contra a maioria dos fatores que causam confusão nas imagens, executando a aproximadamente 10 fps em uma GPU. Ainda, com o intuito de garantir a percepção espacial, foram usados métodos de visão estéreo para projetar as características detectadas em núvem de pontos. Finalmente, foi apresentado também um modelo de localização métrica baseado em visão para validar a aplicabilidade do sistema de percepção proposto em um veículo. Em um experimento, este método de localização revelou-se capaz de manter as estimativas consistentes com a verdadeira pose do veículo em um mapa gerado a partir de um sensor LIDAR.

視覺追蹤(visual tracking)一直是傳統電腦視覺研究中相當重要的議題，許多電腦視覺的應用都需要結合視覺追蹤的幫助才能實現。近年來擴增實境(augmented reality)能快速成功的發展，均有賴於視覺追蹤技術上之精進。擴增實境採用視覺追蹤的技術，可將虛擬的物件呈現在被追蹤的物體(真實場景)上，進而達成所需之應用。 由於在視覺追蹤上，被追蹤之物體易受外在環境因素影響，例如位移、旋轉、縮放、光照改變等，影響追蹤結果之精確度。本研究中，我們設計了一套全新的圖形標記方法作為視覺追蹤之參考點，能降低位移、旋轉與光照改變所造成追蹤結果的誤差，也能在複雜的背景中定位出標記圖形的正確位置，提高視覺追蹤的精確度。同時我們使用立體視覺追蹤物體，將過去只使用單一攝影機於二維影像資訊的追蹤問題，提升至使用三維空間的幾何資訊來做追蹤。然後透過剛體(rigid)特性找出旋轉量、位移量相同的物件，並且結合一致性隨機取樣(random sample consensus)之技巧以估測最佳的剛體物件運動模型，達到強健性追蹤的目的。 另外，我們可由使用者提供之影片資訊中擷取特定資料，透過建模技術將所產生之虛擬物件呈現於使用者介面(或被追蹤之物體)上，並藉由這些虛擬物件，提供真實世界外之資訊，達成導覽指引(或擴增實境)的效果。 實驗結果顯示，我們的方法具有辨識時間快、抗光照變化強、定位準確度高的特性，適合於擴增實境應用，同時我們設計的標記圖形尺寸小，方便適用於導覽指引等應用。 / Visual tracking is one of the most important research topics in traditional computer vision. Many computer vision applications can not be realized without the integration of visual tracking techniques. The fast growing of augmented reality in recent years relied on the improvement of visual tracking technologies. External environment such as object displacement, rotation, and scaling as well as illumination conditions will always influence the accuracy of visual tracking. In this thesis, we designed a set of markers that can reduce the errors induced by the illumination condition changes as well as that by the object displacement, rotation, and scaling. It can also correctly position the markers in complicated background to increase the tracking accuracy. Instead of using single camera tracking in 2D spaces, we used stereo vision techniques to track the objects in 3D spaces. We also used the properties of rigid objects and search for the objects with the same amount of rotation and displacement. Together with the techniques of random sample consensus, we can estimate the best rigid object motion model and achieve tracking robustness. Moreover, from the user supplied video, we can capture particular information and then generate the virtual objects that can be displaced on the user’s device (or on the tracked objects). Using these techniques we can either achieve navigation or guidance in real world or achieve augmented reality as we expected. The experimental results show that our mechanism has the characteristics of fast recognition, accurate positioning, and resisting to illumination changes that are suitable for augmented reality. Also, the size of the markers we designed is very small and good for augmented reality application.

Tracking of human motion and object identification and recognition are important in many applications including motion capture for human-machine interaction systems. This research is part of a global project to enable a service robot to recognize new objects and perform different object-related tasks based on task guidance and demonstration provided by a general user. This research consists of the calibration and testing of two vision systems which are part of a robot-vision system. First, real-time tracking of a human hand is achieved using images acquired from three calibrated synchronized cameras. Hand pose is determined from the positions of physical markers and input to the robot system in real-time. Second, a multi-line laser camera range sensor is designed, calibrated, and mounted on a robot end-effector to provide three-dimensional (3D) geometry information about objects in the robot environment. The laser-camera sensor includes two cameras to provide stereo vision. For the 3D hand tracking, a novel score-based hand tracking scheme is presented employing dynamic multi-threshold marker detection, a stereo camera-pair utilization scheme, marker matching and labeling using epipolar geometry and hand pose axis analysis, to enable real-time hand tracking under occlusion and non-uniform lighting environments. For surface-geometry measurement using the multi-line laser range sensor, two different approaches are analyzed for two-dimensional (2D) to 3D coordinate mapping, using Bezier surface fitting and neural networks, respectively. The neural-network approach was found to be a more viable approach for surface-geometry measurement worth future exploration for its lower magnitude of 3D reconstruction error and consistency over different regions of the object space.

Tracking of human motion and object identification and recognition are important in many applications including motion capture for human-machine interaction systems. This research is part of a global project to enable a service robot to recognize new objects and perform different object-related tasks based on task guidance and demonstration provided by a general user. This research consists of the calibration and testing of two vision systems which are part of a robot-vision system. First, real-time tracking of a human hand is achieved using images acquired from three calibrated synchronized cameras. Hand pose is determined from the positions of physical markers and input to the robot system in real-time. Second, a multi-line laser camera range sensor is designed, calibrated, and mounted on a robot end-effector to provide three-dimensional (3D) geometry information about objects in the robot environment. The laser-camera sensor includes two cameras to provide stereo vision. For the 3D hand tracking, a novel score-based hand tracking scheme is presented employing dynamic multi-threshold marker detection, a stereo camera-pair utilization scheme, marker matching and labeling using epipolar geometry and hand pose axis analysis, to enable real-time hand tracking under occlusion and non-uniform lighting environments. For surface-geometry measurement using the multi-line laser range sensor, two different approaches are analyzed for two-dimensional (2D) to 3D coordinate mapping, using Bezier surface fitting and neural networks, respectively. The neural-network approach was found to be a more viable approach for surface-geometry measurement worth future exploration for its lower magnitude of 3D reconstruction error and consistency over different regions of the object space.