Contribuciones al uso de marcadores para Navegación Autónoma y Realidad Aumentada

Square planar markers are a widely used tools for localization and tracking due to their low cost and high performance. Many applications in Robotics, Unmanned Vehicles and Augmented Reality employ these markers for camera pose estimation with high accuracy. Nevertheless, marker-based systems are affected by several factors that limit their performance. First, the marker detection process is a time-consuming task, which is intensified as the image size increases. As a consequence, the current high-resolution cameras has weakened the processing efficiency of traditional marker systems. Second, marker detection is affected by the presence of noise, blurring and occlusion. The movement of the camera produces image blurriness, generated even by small movements. Furthermore, the marker may be partially or completely occluded in the image, so that it is no longer detected. This thesis deals with the above limitations, proposing novel methodologies and strategies for successful marker detection improving both the efficiency and robustness of these systems. First, a novel multi-scale approach has been developed to speed up the marker detection process. The method takes advantage of the different resolutions at which the image is represented to predict at runtime the optimal scale for detection and identification, as well as following a corner upsampling strategy necessary for an accurate pose estimation. Second, we introduce a new marker design, Fractal Marker, which using a novel keypoint-based method achieves detection even under severe occlusion, while allowing detection over a wider range of distance than traditional markers. Finally, we propose a new marker detection strategy based on Discriminative Correlation Filters (DCF), where the marker and its corners represented in the frequency domain perform more robust and faster detections than state-ofthe- art methods, even under extreme blur conditions.Los marcadores planos cuadrados son una de las herramientas ampliamente utilizadas para la localización y el tracking debido a su bajo coste y su alto rendimiento. Muchas aplicaciones en Robótica, Vehículos no Tripulados y Realidad Aumentada emplean estos marcadores para estimar con alta precisión la posición de la cámara. Sin embargo, los sistemas basados en marcadores se ven afectados por varios factores que limitan su rendimiento. En primer lugar, el proceso de detección de marcadores es una tarea que requiere mucho tiempo y este incrementa a medida que aumenta el tamaño de la imagen. En consecuencia, las actuales cámaras de alta resolución han debilitado la eficacia del procesamiento de los sistemas de marcadores tradicionales. Por otra parte, la detección de marcadores se ve afectada por la presencia de ruido, desenfoque y oclusión. El movimiento de la cámara produce desenfoque de la imagen, generado incluso por pequeños movimientos. Además, el marcador puede aparecer en la imagen parcial o completamente ocluido, dejando de ser detectado. Esta tesis aborda las limitaciones anteriores, proponiendo metodologías y estrategias novedosas para la correcta detección de marcadores, mejorando así tanto la eficiencia como la robustez de estos sistemas. En primer lugar, se ha desarrollado un novedoso enfoque multiescala para acelerar el proceso de detección de marcadores. El método aprovecha las diferentes resoluciones en las que la imagen está representada para predecir en tiempo de ejecución la escala óptima para la detección e identificación, a la vez que sigue una estrategia de upsampling de las esquinas necesaria para estimar la pose con precisión. En segundo lugar, introducimos un nuevo diseño de marcador, Fractal Marker, que, mediante un método basado en keypoints, logra detecciones incluso en casos de oclusión extrema, al tiempo que permite la detección en un rango de distancias más amplio que los marcadores tradicionales. Por último, proponemos una nueva estrategia de detección de marcadores basada en Discriminate Correlation Filters (DCF), donde el marcador y sus esquinas representadas en el dominio de la frecuencia realizan detecciones más robustas y rápidas que los métodos de referencia, incluso bajo condiciones extremas de emborronamiento

Contribuciones a la estimación de la pose de la cámara en aplicaciones industriales de realidad aumentada

Augmented Reality (AR) aims to complement the visual perception of the user environment superimposing virtual elements. The main challenge of this technology is to combine the virtual and real world in a precise and natural way. To carry out this goal, estimating the user position and orientation in both worlds at all times is a crucial task. Currently, there are numerous techniques and algorithms developed for camera pose estimation. However, the use of synthetic square markers has become the fastest, most robust and simplest solution in these cases. In this scope, a big number of marker detection systems have been developed. Nevertheless, most of them presents some limitations, (1) their unattractive and non-customizable visual appearance prevent their use in industrial products and (2) the detection rate is drastically reduced in presence of noise, blurring and occlusions. In this doctoral dissertation the above-mentioned limitations are addressed. In first place, a comparison has been made between the different marker detection systems currently available in the literature, emphasizing the limitations of each. Secondly, a novel approach to design, detect and track customized markers capable of easily adapting to the visual limitations of commercial products has been developed. In third place, a method that combines the detection of black and white square markers with keypoints and contours has been implemented to estimate the camera position in AR applications. The main motivation of this work is to offer a versatile alternative (based on contours and keypoints) in cases where, due to noise, blurring or occlusions, it is not possible to identify markers in the images. Finally, a method for reconstruction and semantic segmentation of 3D objects using square markers in photogrammetry processes has been presented.La Realidad Aumentada (AR) tiene como objetivo complementar la percepción visual del entorno circunstante al usuario mediante la superposición de elementos virtuales. El principal reto de dicha tecnología se basa en fusionar, de forma precisa y natural, el mundo virtual con el mundo real. Para llevar a cabo dicha tarea, es de vital importancia conocer en todo momento tanto la posición, así como la orientación del usuario en ambos mundos. Actualmente, existen un gran número de técnicas de estimación de pose. No obstante, el uso de marcadores sintéticos cuadrados se ha convertido en la solución más rápida, robusta y sencilla utilizada en estos casos. En este ámbito de estudio, existen un gran número de sistemas de detección de marcadores ampliamente extendidos. Sin embargo, su uso presenta ciertas limitaciones, (1) su aspecto visual, poco atractivo y nada customizable impiden su uso en ciertos productos industriales en donde la personalización comercial es un aspecto crucial y (2) la tasa de detección se ve duramente decrementada ante la presencia de ruido, desenfoques y oclusiones Esta tesis doctoral se ocupa de las limitaciones anteriormente mencionadas. En primer lugar, se ha realizado una comparativa entre los diferentes sistemas de detección de marcadores actualmente en uso, enfatizando las limitaciones de cada uno. En segundo lugar, se ha desarrollado un novedoso enfoque para diseñar, detectar y trackear marcadores personalizados capaces de adaptarse fácilmente a las limitaciones visuales de productos comerciales. En tercer lugar, se ha implementado un método que combina la detección de marcadores cuadrados blancos y negros con keypoints y contornos, para estimar de la posición de la cámara en aplicaciones AR. La principal motivación de este trabajo se basa en ofrecer una alternativa versátil (basada en contornos y keypoints) en aquellos casos donde, por motivos de ruido, desenfoques u oclusiones no sea posible identificar marcadores en las imágenes. Por último, se ha desarrollado un método de reconstrucción y segmentación semántica de objetos 3D utilizando marcadores cuadrados en procesos de fotogrametría

Application of augmented reality and robotic technology in broadcasting: A survey

As an innovation technique, Augmented Reality (AR) has been gradually deployed in the broadcast, videography and cinematography industries. Virtual graphics generated by AR are dynamic and overlap on the surface of the environment so that the original appearance can be greatly enhanced in comparison with traditional broadcasting. In addition, AR enables broadcasters to interact with augmented virtual 3D models on a broadcasting scene in order to enhance the performance of broadcasting. Recently, advanced robotic technologies have been deployed in a camera shooting system to create a robotic cameraman so that the performance of AR broadcasting could be further improved, which is highlighted in the paper

Indoor and outdoor localization for AGVs in the primary aluminum industry

The goal of this project is to analyze two types of AGV indoor and outdoor localization techniques in an aluminum smelter building with a particular operative vehicle. The indoor localization is performed with the ARTag markers system while the outdoor localization employs multiple Wi-Fi transceivers to trilaterate the position of the vehicle based on the RSSI value. Finally, raw estimated pose is fused with the IMU sensor data using the extended kalman filter to increase localization accuracy.openEmbargo tempraneo per motivi di segretezza e/o di proprietà dei risultati e informazioni di enti esterni o aziende private che hanno partecipato alla realizzazione del lavoro di ricerca relativo alla tes

Image Space Coverage Model for Deployment of Multi-Camera Networks

When it comes to visual sensor networks deployment and optimization, modeling the coverage of a given camera network is a vital step. Due to many complex parameters and criteria that governs coverage quality of a given visual network, modeling such coverage accurately and efficiently represents a real challenge. This thesis explores the idea of simplifying the mathematical interpretation that describes a given visual sensor without incurring a cost on coverage measurement accuracy. In this thesis, coverage criteria are described in image space, in contrast to some of the more advanced models found in literature, that are formulated in 3D space, which in turn will have a direct impact on efficiency and time cost. In addition, this thesis also proposes a novel sensor deployment approach that examines the surface topology of the target object to be covered by means of a mesh segmentation algorithm, which is that a different way to tackle the problem other than the exhaustive search methods employed in the examined literature. There are two main contributions in this thesis. Firstly, a new coverage model that takes partial occlusion criterion into account is proposed, which is shown to be more accurate and more efficient than the competition. Next, a new sensor deployment method was presented that takes the target object shape topological properties into account, an approach that is to the best of our knowledge, was not attempted in literature before at the time of publication. This thesis attempts to support all of claims made above, the proposed model is validated and compared to an existing state of art coverage model. In addition, simulations and experiments were carried out to demonstrate the accuracy and time cost efficiency of the proposed work

Projection-based Spatial Augmented Reality for Interactive Visual Guidance in Surgery

Ph.DDOCTOR OF PHILOSOPH

Marker-free surgical navigation of rod bending using a stereo neural network and augmented reality in spinal fusion

The instrumentation of spinal fusion surgeries includes pedicle screw placement and rod implantation. While several surgical navigation approaches have been proposed for pedicle screw placement, less attention has been devoted towards the guidance of patient-specific adaptation of the rod implant. We propose a marker-free and intuitive Augmented Reality (AR) approach to navigate the bending process required for rod implantation. A stereo neural network is trained from the stereo video streams of the Microsoft HoloLens in an end-to-end fashion to determine the location of corresponding pedicle screw heads. From the digitized screw head positions, the optimal rod shape is calculated, translated into a set of bending parameters, and used for guiding the surgeon with a novel navigation approach. In the AR-based navigation, the surgeon is guided step-by-step in the use of the surgical tools to achieve an optimal result. We have evaluated the performance of our method on human cadavers against two benchmark methods, namely conventional freehand bending and marker-based bending navigation in terms of bending time and rebending maneuvers. We achieved an average bending time of 231s with 0.6 rebending maneuvers per rod compared to 476s (3.5 rebendings) and 348s (1.1 rebendings) obtained by our freehand and marker-based benchmarks, respectively

Advances in top-down and bottom-up approaches to video-based camera tracking

Video-based camera tracking consists in trailing the three dimensional pose followed by a mobile camera using video as sole input. In order to estimate the pose of a camera with respect to a real scene, one or more three dimensional references are needed. Examples of such references are landmarks with known geometric shape, or objects for which a model is generated beforehand. By comparing what is seen by a camera with what is geometrically known from reality, it is possible to recover the pose of the camera that is sensing these references. In this thesis, we investigate the problem of camera tracking at two levels. Firstly, we work at the low level of feature point recognition. Feature points are used as references for tracking and we propose a method to robustly recognise them. More specifically, we introduce a rotation-discriminative region descriptor and an efficient rotation-discriminative method to match feature point descriptors. The descriptor is based on orientation gradient histograms and template intensity information. Secondly, we have worked at the higher level of camera tracking and propose a fusion of top-down (TDA) and bottom-up approaches (BUA). We combine marker-based tracking using a BUA and feature points recognised from a TDA into a particle filter. Feature points are recognised with the method described before. We take advantage of the identification of the rotation of points for tracking purposes. The goal of the fusion is to take advantage of their compensated strengths. In particular, we are interested in covering the main capabilities that a camera tracker should provide. These capabilities are automatic initialisation, automatic recovery after loss of track, and tracking beyond references known a priori. Experiments have been performed at the two levels of investigation. Firstly, tests have been conducted to evaluate the performance of the recognition method proposed. The assessment consists in a set of patches extracted from eight textured images. The images are rotated and matching is done for each patch. The results show that the method is capable of matching accurately despite the rotations. A comparison with similar techniques in the state of the art depicts the equal or even higher precision of our method with much lower computational cost. Secondly, experimental assessment of the tracking system is also conducted. The evaluation consists in four sequences with specific problematic situations namely, occlusions of the marker, illumination changes, and erratic and/or fast motion. Results show that the fusion tracker solves characteristic failure modes of the two combined approaches. A comparison with similar trackers shows competitive accuracy. In addition, the three capabilities stated earlier are fulfilled in our tracker, whereas the state of the art reveals that no other published tracker covers these three capabilities simultaneously. The camera tracking system has a potential application in the robotics domain. It has been successfully used as a man-machine interface and applied in Augmented Reality environments. In particular, the system has been used by students of the University of art and design Lausanne (ECAL) with the purpose of conceiving new interaction concepts. Moreover, in collaboration with ECAL and fabric | ch (studio for architecture & research), we have jointly developed the Augmented interactive Reality Toolkit (AiRToolkit). The system has also proved to be reliable in public events and is the basis of a game-oriented demonstrator installed in the Swiss National Museum of Audiovisual and Multimedia (Audiorama) in Montreux