Unmanned Aerial Vehicle Ground Control Point Deployment

According to Federal Aviation Regulation (FAR) Part 77, all airports have imaginary approach surfaces which must remain clear of obstructions in order to ensure safe air travel. Threats of penetration to these imaginary surfaces include new construction, telephone poles and lines, and trees. While most potential threats analyzed remain relatively constant in size, objects such as trees which grow require annual analysis for change detection. A variety of methods are available for surveying these surfaces for potential obstructions, one being an aerial mapping from photogrammetric data. Aerial mapping for surveying purposes is a process which ties overlapping photographs together using computer software which detects similar points between the images. These images requires ground control points, also known as GCPs, to create a scale which allows for accurate measurement data. When ground control points with known GPS locations are placed throughout the mapping area all the points within the model can then be tied to their respective GPS coordinates in the longitudinal, latitudinal, and altitude directions. The placement of these markers is one of the most time-consuming but necessary tasks when creating an aerial map. The main objective of this project was for the team to design and produce a method of streamlining the ground control point deployment process. This report introduces a device which, when implemented, can reduce the number of resources and labor needed for this process. A mechanical release system was designed and built to be carried by a drone. Remote control between two XBee RF modules was implemented to activate the rotating notch release system, following user commands. A 90 degree rotation by the servo of the flange would align the flange with the keyslot in the GCP, allowing it to fall. The final design was successfully operated by one pilot and three GCP’s were deployed at various locations. An important factor in designing this system was developing lightweight, high contrast GCPs that would not impact the flight of the drone. The weight and balance of the final product were suitable for the small 3DR Solo drone used in the project. This accomplishment proves this system could be applied to drones with greater payloads and longer flight times for preparing large surveying areas in minimal time

A strategy for the visual recognition of objects in an industrial environment.

This thesis is concerned with the problem of recognizing industrial objects rapidly and flexibly. The system design is based on a general strategy that consists of a generalized local feature detector, an extended learning algorithm and the use of unique structure of the objects. Thus, the system is not designed to be limited to the industrial environment. The generalized local feature detector uses the gradient image of the scene to provide a feature description that is insensitive to a range of imaging conditions such as object position, and overall light intensity. The feature detector is based on a representative point algorithm which is able to reduce the data content of the image without restricting the allowed object geometry. Thus, a major advantage of the local feature detector is its ability to describe and represent complex object structure. The reliance on local features also allows the system to recognize partially visible objects. The task of the learning algorithm is to observe the feature description generated by the feature detector in order to select features that are reliable over the range of imaging conditions of interest. Once a set of reliable features is found for each object, the system finds unique relational structure which is later used to recognize the objects. Unique structure is a set of descriptions of unique subparts of the objects of interest. The present implementation is limited to the use of unique local structure. The recognition routine uses these unique descriptions to recognize objects in new images. An important feature of this strategy is the transference of a large amount of processing required for graph matching from the recognition stage to the learning stage, which allows the recognition routine to execute rapidly. The test results show that the system is able to function with a significant level of insensitivity to operating conditions; The system shows insensitivity to its 3 main assumptions -constant scale, constant lighting, and 2D images- displaying a degree of graceful degradation when the operating conditions degrade. For example, for one set of test objects, the recognition threshold was reached when the absolute light level was reduced by 70%-80%, or the object scale was reduced by 30%-40%, or the object was tilted away from the learned 2D plane by 300-400. This demonstrates a very important feature of the learning strategy: It shows that the generalizations made by the system are not only valid within the domain of the sampled set of images, but extend outside this domain. The test results also show that the recognition routine is able to execute rapidly, requiring 10ms-500ms (on a PDP11/24 minicomputer) in the special case when ideal operating conditions are guaranteed. (Note: This does not include pre-processing time). This thesis describes the strategy, the architecture and the implementation of the vision system in detail, and gives detailed test results. A proposal for extending the system to scale independent 3D object recognition is also given

A survey of real-time crowd rendering

In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft

YOLO Object Detector for Onboard Driving Images

With the evolution of artificial intelligence and, specially, machine learning, tech and car manufacturing companies are in research of the car of the future. Along with the arrival of new powerful hardware, deep learning is expected to be one of the most outstanding fields in the automotive sector. In this paper, we will be developing an object detection system with neural networks using the You Only Look Once (YOLO) network architecture. We will train and evaluate the model using various datasets and extract conclusions on its feasibility for autonomous driving or other driving assistance applications.Con la evolución de la inteligencia artificial y, especialmente, el aprendizaje computacional, las empresas fabricantes de automóviles y de tecnología están en plena investigación del coche del futuro. Junto con la llegada de nuevo hardware potente, el aprendizaje profundo se espera que sea uno de los campos más destacados en el sector automotriz. En este trabajo, estaremos desarrollando un sistema de detección de objetos con redes neuronales utilizando la arquitectura de You Only Look Once (YOLO). Vamos a entrenar y evaluar el modelo utilizando varios conjuntos de datos y extraer conclusiones sobre su viabilidad para la conducción autónoma u otras aplicaciones de asistencia a la conducción.Amb l'evolució de la intel·ligència artificial i, especialment, l'aprenentatge computacional, les empreses fabricants d'automòbils i de tecnologia estan en plena investigació del cotxe del futur. Juntament amb l'arribada de nou maquinari potent, l'aprenentatge profund s'espera que sigui un dels camps més destacats en el sector de l'automòbil. En aquest treball, estarem desenvolupant un sistema de detecció d'objectes amb xarxes neuronals utilitzant l'arquitectura de You Only Look Onze (YOLO). Entrenarem i avaluarem el model utilitzant diversos conjunts de dades per poder extreure conclusions sobre la seva viabilitat per a la conducció autònoma o altres aplicacions d'assistència a la conducció

Crab Tracker Documentation

User and technical documentation for the Crab Tracker project, including details on system architecture, configuration, and design

3D digital relief generation.

This thesis investigates a framework for generating reliefs. Relief is a special kind of sculptured artwork consisting of shapes carved on a surface so as to stand out from the surrounding background. Traditional relief creation is done by hand and is therefore a laborious process. In addition, hand-made reliefs are hard to modify. Contrasted with this, digital relief can offer more flexibility as well as a less laborious alternative and can be easily adjusted. This thesis reviews existing work and offers a framework to tackle the problem of generating three types of reliefs: bas reliefs, high reliefs and sunken reliefs. Considerably enhanced by incorporating gradient operations, an efficient bas relief generation method has been proposed, based on 2D images. An improvement of bas relief and high relief generation method based on 3D models has been provided as well, that employs mesh representation to process the model. This thesis is innovative in describing and evaluating sunken relief generation techniques. Two types of sunken reliefs have been generated: one is created with pure engraved lines, and the other is generated with smooth height transition between lines. The latter one is more complex to implement, and includes three elements: a line drawing image provides a input for contour lines; a rendered Lambertian image shares the same light direction of the relief and sets the visual cues and a depth image conveys the height information. These three elements have been combined to generate final sunken reliefs. It is the first time in computer graphics that a method for digital sunken relief generation has been proposed. The main contribution of this thesis is to have proposed a systematic framework to generate all three types of reliefs. Results of this work can potentially provide references for craftsman, and this work could be beneficial for relief creation in the fields of both entertainment and manufacturing