Micro Aerial Vehicles (MAVs) provide a highly capable, agile platform, ideally suited for intelligence/surveillance/reconnaissance missions, urban search and rescue, and scientific exploration. Critical to the success of these tasks is a system which moves au-tonomously through an unknown, obstacle-strewn, GPS-denied environment. Classical simultaneous localization and mapping (SLAM) approaches rely on large, heavy sensors to generate 3-D information about a MAV’s surroundings, severely limiting its abilities. This motivates a study of Parallel Tracking and Mapping (PTAM), an algorithm requiring only a single camera to provide 3-D data to an autonomous navigation system. Metric properties of 3-D MAV pose estimates are compared with physical measurements to ex-plore tracking accuracy. Additionally, a discrete wavelet transform-based keypoint detec-tor is implemented for a feasibility study on improving map density in low-visual-detail environments. Finally, a system is presented that integrates PTAM, autonomous MAV control, and a human interface for manual control and data logging.

To map the subsurface Ground Penetrating Radar (GPR) can be used in a non-invasive way. It is currently done manually by pushing a wheeled device on a handlebar. This thesis suggests an alternative method using an integrated autonomous solution. To ac- complice that: several sensors were fused to give the robot perception of the world, the ability to localize itself within it, and plan a path to reach the goal. Detecting algorithms were implemented and tested to ensure the robot could handle a dynamic and compli- cated world. The results showed that the robot could independently navigate in a grid pattern conducting GPR surveys while avoiding obstacles and finding a safe route. All this will allow for collecting GPR data with precise localization measurements and in paths more detailed than a human operator could. In addition, it enables the operator to be at a safe distance in dangerous environments and to search large areas.

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The clothoid, also known as the Cornu spiral, is a curve generated by linearly increasing or decreasing curvature as a function of arc length. The clothoid has been widely accepted as a logical curve for transitioning from straight segments to circle arcs in roads and railways, because a vehicle following the curve at constant speed will have a constant change of centripetal acceleration. Clothoids have also been widely adopted in planning potential paths for autonomous vehicle navigation. They have been viewed as useful representations of possible trajectories that are dynamically feasible. Surprisingly, the assumptions that underlie this choice appear to be lightly treated or ignored in past literature. This thesis will examine three key assumptions that are implicitly made when assuming that a vehicle will follow a clothoid path. The first assumption is that the vehicle's steering mechanism will produce a linear change in turning radius for a constant rate input. This assumption is loosely referred to as the "bicycle model" and it relates directly to the kinematic parameters of the steering mechanism. The second assumption is that the steering actuator can provide a constant steering velocity. In other words, the actuator controlling the steering motion can instantaneously change from one rate to another. The third assumption is that the vehicle is traveling at a constant velocity. By definition, the clothoid is a perfect representation of a vehicle traveling at constant velocity with a constant rate of change in steering curvature. The goal of this research was to examine the accuracy of these assumptions for a typical Ackermann-steered ground vehicle. Both theoretical and experimental results are presented. The vehicle that was used as an example in this study was a modified Club Car Pioneer XRT 1500. This Ackermann-steered vehicle was modified for autonomous navigation and was one of Virginia Tech's entries in the DARPA 2005 Grand Challenge. As in typical operation, path planning was conducted using the classic clothoid curve model. The vehicle was then commanded to drive a selected path, but with variations in speed and steering rate that are inherent to the real system. The validity of the three assumptions discussed above were examined by comparing the actual vehicle response to the planned clothoid. This study determined that the actual paths driven by the vehicle were generally a close match to the originally planned theoretical clothoid path. In this study, the actual kinematics of the Ackermann vehicle steering system had only a small effect on the driven path. This indicates that the bicycle model is a reasonable simplification, at least for the case studied. The assumption of constant velocity actuation of the steering system also proved to be reasonably accurate. The greatest deviation from the planned clothoid path resulted from the nonlinear velocity of the vehicle along the path, especially when accelerating from a stop. Nevertheless, the clothoid path plan generally seems to be a good representation of actual vehicle motion, especially when the planned path is updated frequently. / Master of Science

This thesis focuses on the theory and implementation of visual navigation techniques for Autonomous Air Vehicles in outdoor environments. The target of this study is to fuse and cooperatively develop an incremental map for multiple air vehicles under the application of Simultaneous Location and Mapping (SLAM). Without loss of generality, two unmanned air vehicles (UAVs) are investigated for the generation of ground maps from current and a priori data. Each individual UAV is equipped with inertial navigation systems and external sensitive elements which can provide the possible mixture of visible, thermal infrared (IR) image sensors, with a special emphasis on the stereo digital cameras. The corresponding stereopsis is able to provide the crucial three-dimensional (3-D) measurements. Therefore, the visual aerial navigation problems tacked here are interpreted as stereo vision based SLAM (vSLAM) for both single and multiple UAVs applications. To begin with, the investigation is devoted to the methodologies of feature extraction. Potential landmarks are selected from airborne camera images as distinctive points identified in the images are the prerequisite for the rest. Feasible feature extraction algorithms have large influence over feature matching/association in 3-D mapping. To this end, effective variants of scale-invariant feature transform (SIFT) algorithms are employed to conduct comprehensive experiments on feature extraction for both visible and infrared aerial images. As the UAV is quite often in an uncertain location within complex and cluttered environments, dense and blurred images are practically inevitable. Thus, it becomes a challenge to find feature correspondences, which involves feature matching between 1st and 2nd image in the same frame, and data association of mapped landmarks and camera measurements. A number of tests with different techniques are conducted by incorporating the idea of graph theory and graph matching. The novel approaches, which could be tagged as classification and hypergraph transformation (HGTM) based respectively, have been proposed to solve the data association in stereo vision based navigation. These strategies are then utilised and investigated for UAV application within SLAM so as to achieve robust matching/association in highly cluttered environments. The unknown nonlinearities in the system model, including noise would introduce undesirable INS drift and errors. Therefore, appropriate appraisals on the pros and cons of various potential data filtering algorithms to resolve this issue are undertaken in order to meet the specific requirements of the applications. These filters within visual SLAM were put under investigation for data filtering and fusion of both single and cooperative navigation. Hence updated information required for construction and maintenance of a globally consistent map can be provided by using a suitable algorithm with the compromise between computational accuracy and intensity imposed by the increasing map size. The research provides an overview of the feasible filters, such as extended Kalman Filter, extended Information Filter, unscented Kalman Filter and unscented H Infinity Filter. As visual intuition always plays an important role for humans to recognise objects, research on 3-D mapping in textures is conducted in order to fulfil the purpose of both statistical and visual analysis for aerial navigation. Various techniques are proposed to smooth texture and minimise mosaicing errors during the reconstruction of 3-D textured maps with vSLAM for UAVs. Finally, with covariance intersection (CI) techniques adopted on multiple sensors, various cooperative and data fusion strategies are introduced for the distributed and decentralised UAVs for Cooperative vSLAM (C-vSLAM). Together with the complex structure of high nonlinear system models that reside in cooperative platforms, the robustness and accuracy of the estimations in collaborative mapping and location are achieved through HGTM association and communication strategies. Data fusion among UAVs and estimation for visual navigation via SLAM were impressively verified and validated in conditions of both simulation and real data sets.

O emprego de veículos autônomos é uma prática comumente adotada para a melhoria da produtividade no setor agrícola. No entanto, o custo computacional é um fator limitante na implementação desses dispositivos autônomos. A alternativa apresentada neste trabalho consistiu no desenvolvimento de um dispositivo de hardware dedicado para a navegação de robôs móveis agrícolas, o qual indica áreas navegáveis e não navegáveis, além do ângulo de inclinação do veículo em relação à linha de plantio. O desenvolvimento do projeto foi baseado em um método de extração de características visuais locais por meio do processamento de imagens coloridas obtidas por uma câmera de vídeo. O circuito foi implementado por meio de uma ferramenta de desenvolvimento baseado em um FPGA de baixo custo. O circuito consiste nas etapas de classificação, processamento morfológico e extração das linhas de navegação. Na primeira etapa, os pixels são classificados a partir do modelo de cores HSL em classes que representam as áreas passíveis e não passíveis de navegação. Posteriormente, a etapa de processamento morfológico realiza as tarefas de filtragem, agrupamento e extração de bordas. O processamento morfológico é realizado por meio de um arranjo de unidades de processamento dedicadas. Cada unidade pode realizar uma operação básica de morfologia matemática. O elemento estruturante utilizado na operação, bem como a operação realizada pela unidade, é configurado por meio de parâmetros do projeto. O processo de extração das linhas de orientação é realizado por meio do método de regressão linear por mínimos quadrados. A arquitetura proposta no projeto permitiu o processamento em tempo real de imagens para a aplicação de navegação autônoma de robôs móveis em ambientes agrícolas. / The use of autonomous vehicles is a generally adopted practice to improve the productivity in the agriculture sector. However, the computer requirements are a limiting factor for implementation of these autonomous devices. The alternative shown in this paper is the design of a dedicated hardware for the autonomous agricultural robot navigation. The project development was based on a local visual feature extraction method by processing digital images obtained from a color video camera. The circuit was implemented through a development tool based on a low cost FPGA. The circuit consists of stages of classification, morphological processing and guidance line extraction. In the first stage, the pixels are classified through HSL color model into classes that represent suitable and unsuitable area for navigation. Then, the morphological processing stage performs filtering, grouping and edge detection tasks. The morphological processing is carried out by an arrangement of dedicated processing units. Each unit can perform a basic operation of mathematical morphology. The structuring element used in the operation and the operation performed by the unit are configured through project parameters. The guidance line extraction process is performed through the linear regression method by least square. The architecture proposed in the design allowed the real-time image processing in autonomous robot navigation applications in agricultural environments.

O emprego de veículos autônomos é uma prática comumente adotada para a melhoria da produtividade no setor agrícola. No entanto, o custo computacional é um fator limitante na implementação desses dispositivos autônomos. A alternativa apresentada neste trabalho consistiu no desenvolvimento de um dispositivo de hardware dedicado para a navegação de robôs móveis agrícolas, o qual indica áreas navegáveis e não navegáveis, além do ângulo de inclinação do veículo em relação à linha de plantio. O desenvolvimento do projeto foi baseado em um método de extração de características visuais locais por meio do processamento de imagens coloridas obtidas por uma câmera de vídeo. O circuito foi implementado por meio de uma ferramenta de desenvolvimento baseado em um FPGA de baixo custo. O circuito consiste nas etapas de classificação, processamento morfológico e extração das linhas de navegação. Na primeira etapa, os pixels são classificados a partir do modelo de cores HSL em classes que representam as áreas passíveis e não passíveis de navegação. Posteriormente, a etapa de processamento morfológico realiza as tarefas de filtragem, agrupamento e extração de bordas. O processamento morfológico é realizado por meio de um arranjo de unidades de processamento dedicadas. Cada unidade pode realizar uma operação básica de morfologia matemática. O elemento estruturante utilizado na operação, bem como a operação realizada pela unidade, é configurado por meio de parâmetros do projeto. O processo de extração das linhas de orientação é realizado por meio do método de regressão linear por mínimos quadrados. A arquitetura proposta no projeto permitiu o processamento em tempo real de imagens para a aplicação de navegação autônoma de robôs móveis em ambientes agrícolas. / The use of autonomous vehicles is a generally adopted practice to improve the productivity in the agriculture sector. However, the computer requirements are a limiting factor for implementation of these autonomous devices. The alternative shown in this paper is the design of a dedicated hardware for the autonomous agricultural robot navigation. The project development was based on a local visual feature extraction method by processing digital images obtained from a color video camera. The circuit was implemented through a development tool based on a low cost FPGA. The circuit consists of stages of classification, morphological processing and guidance line extraction. In the first stage, the pixels are classified through HSL color model into classes that represent suitable and unsuitable area for navigation. Then, the morphological processing stage performs filtering, grouping and edge detection tasks. The morphological processing is carried out by an arrangement of dedicated processing units. Each unit can perform a basic operation of mathematical morphology. The structuring element used in the operation and the operation performed by the unit are configured through project parameters. The guidance line extraction process is performed through the linear regression method by least square. The architecture proposed in the design allowed the real-time image processing in autonomous robot navigation applications in agricultural environments.

<p>The field of autonomous vehicle navigation and localization is a highly active research</p><p>topic. The aim of this thesis is to evaluate the feasibility to use outdoor visual navigation in a semi-structured environment. The goal is to develop a visual navigation system for an autonomous golf ball collection vehicle operating on driving ranges.</p><p>The image feature extractors SIFT and PCA-SIFT was evaluated on an image database</p><p>consisting of images acquired from 19 outdoor locations over a period of several weeks to</p><p>allow different environmental conditions. The results from these tests show that SIFT-type</p><p>feature extractors are able to find and match image features with high accuracy. The results also show that this can be improved further by a combination of a lower nearest neighbour threshold and an outlier rejection method to allow more matches and a higher ratio of correct matches. Outliers were found and rejected by fitting the data to a homography model with the RANSAC robust estimator algorithm. </p><p>A simulator was developed to evaluate the suggested system with respect to pixel noise from illumination changes, weather and feature position accuracy as well as the distance to features, path shapes and the visual servoing target image (milestone) interval. The system was evaluated on a total of 3 paths, 40 test combinations and 137km driven. The results show that with the relatively simple visual servoing navigation system it is possible to use mono-vision as a sole sensor and navigate semi-structured outdoor environments such as driving ranges.</p>