Fusion of Imaging and Inertial Sensors for Navigation

The motivation of this research is to address the limitations of satellite-based navigation by fusing imaging and inertial systems. The research begins by rigorously describing the imaging and navigation problem and developing practical models of the sensors, then presenting a transformation technique to detect features within an image. Given a set of features, a statistical feature projection technique is developed which utilizes inertial measurements to predict vectors in the feature space between images. This coupling of the imaging and inertial sensors at a deep level is then used to aid the statistical feature matching function. The feature matches and inertial measurements are then used to estimate the navigation trajectory using an extended Kalman filter. After accomplishing a proper calibration, the image-aided inertial navigation algorithm is then tested using a combination of simulation and ground tests using both tactical and consumer- grade inertial sensors. While limitations of the Kalman filter are identified, the experimental results demonstrate a navigation performance improvement of at least two orders of magnitude over the respective inertial-only solutions

Large Scale Acquisition of Complex Environments by Data Fusion from Mobile Visual and Depth Sensors

This thesis addresses the pressing need in industry and Architecture, Engineering, and Construction (AEC) for efficient scanning and modeling of large, structured environments. The primary objective is the development and application of a state-of-the-art wearable system integrating laser and visual scanning technology. This innovative mobile mapping system (MMS) is designed to capture complex man-made structures in diverse settings, ranging from indoor spaces to challenging outdoor environments and underground settings. The MMS combines high-resolution photographic data with laser scanning and inertial inputs to generate detailed, dense point clouds, offering extensive coverage and depth. This approach addresses the limitations of geometric models by providing photorealistic representations essential for applications requiring accurate location recognition, object identification, and content creation. Key contributions of this research include the development of the MMS prototype, capable of adapting to various data acquisition scenarios while ensuring scalability and minimal data redundancy. Additionally, the thesis explores the practical application of the MMS in a pilot AEC project, demonstrating its effectiveness in real-world scenarios for construction monitoring and integration with Building Information Modeling (BIM). Through a combination of technical development, rigorous testing, and practical application, this thesis advances the field of mobile mapping. It opens new avenues for spatial data acquisition and modeling, particularly in environments where traditional mapping techniques fall short

Information Theory and Probabilistic Modeling For Robot Localization

This dissertation presents three contributions to visual perception for localization of mobile robots, based on probabilistic modeling and information theory. First, we present the hidden Markov random field iterated closest point algorithm. When registering 3-D point clouds it is expected that some points in one cloud do not have corresponding points in the other cloud. These non-correspondences are likely to occur near one another, as surface regions visible from one sensor pose are obscured or out of frame for another. In this contribution, a hidden Markov random field model is used to capture this prior within the framework of the iterative closest point algorithm. The EM algorithm is used to estimate the distribution parameters and learn the hidden component memberships. By robustly inferring which points are in the overlap, the non-overlapping points can be ignored while aligning the point clouds. Experiments are presented demonstrating that this method outperforms several other outlier rejection methods when the point clouds have low or moderate overlap. Second, we present a method for active gaze control for localization. Sparse visual-inertial odometry relies on visual features in the environment to extract useful information from cameras. However, the distribution of these features, especially in built environments, is far from uniform, leading to loss-of-tracking failures if the camera happens to be directed at a suboptimal view. Active gaze control, directing a camera towards informative features in the environment, can improve visual-inertial localization of a mobile robot. In this contribution, informative gazes are selected using a map of landmarks and the anticipated robot trajectory. We develop an attention mechanism that executes a branch-and-bound search over potential fixations to find the optimal view, and develop a heuristic for the search specific to this problem. The method is demonstrated and verified in a simulated environment and in a real dataset taken with a fisheye lens, from which a small region of interest is selected. The results indicate that a mechanically articulated camera system is a worthwhile endeavour. Finally, we consider an information-theoretic approach to keyframing. In visual-intertial odometry, some camera frames are designated keyframes and retained in the estimation as newer (non-key) frames are discarded. We present a method for choosing between keeping the oldest keyframe or inserting a new keyframe based on the observed Fisher information of the resulting pose estimates. Unfortunately, the resulting method does not outperform existing keyframe heuristics, as the decision must be made using only the pose estimates at the current timestep, while keyframes will persist for several more steps. In these contributions, probabilistic modeling and information theory provide the theoretical framework to advance the capabilities of robotic localization.</p

Data fusion architecture for intelligent vehicles

Traffic accidents are an important socio-economic problem. Every year, the cost in human lives and the economic consequences are inestimable. During the latest years, efforts to reduce or mitigate this problem have lead to a reduction in casualties. But, the death toll in road accidents is still a problem, which means that there is still much work to be done. Recent advances in information technology have lead to more complex applications, which have the ability to help or even substitute the driver in case of hazardous situations, allowing more secure and efficient driving. But these complex systems require more trustable and accurate sensing technology that allows detecting and identifying the surrounding environment as well as identifying the different objects and users. However, the sensing technology available nowadays is insufficient itself, and thus combining the different available technologies is mandatory in order to fulfill the exigent requirements of safety road applications. In this way, the limitations of every system are overcome. More dependable and reliable information can be thus obtained. These kinds of applications are called Data Fusion (DF) applications. The present document tries to provide a solution for the Data Fusion problem in the Intelligent Transport System (ITS) field by providing a set of techniques and algorithms that allow the combination of information from different sensors. By combining these sensors the basic performances of the classical approaches in ITS can be enhanced, satisfying the demands of safety applications. The works presented are related with two researching fields. Intelligent Transport System is the researching field where this thesis was established. ITS tries to use the recent advances in Information Technology to increase the security and efficiency of the transport systems. Data Fusion techniques, on the other hand, try to give solution to the process related with the combination of information from different sources, enhancing the basic capacities of the systems and adding trustability to the inferences. This work attempts to use the Data Fusion algorithms and techniques to provide solution to classic ITS applications. The sensors used in the present application include a laser scanner and computer vision. First is a well known sensor, widely used, and during more recent years have started to be applied in different ITS applications, showing advanced performance mainly related to its trustability. Second is a recent sensor in automotive applications widely used in all recent ITS advances in the last decade. Thanks to computer vision road security applications (e.g. traffic sign detection, driver monitoring, lane detection, pedestrian detection, etc.) advancements are becoming possible. The present thesis tries to solve the environment reconstruction problem, identifying users of the roads (i.e. pedestrians and vehicles) by the use of Data Fusion techniques. The solution delivers a complete level based solution to the Data Fusion problem. It provides different tools for detecting as well as estimates the degree of danger that involve any detection. Presented algorithms represents a step forward in the ITS world, providing novel Data Fusion based algorithms that allow the detection and estimation of movement of pedestrians and vehicles in a robust and trustable way. To perform such a demanding task other information sources were needed: GPS, inertial systems and context information. Finally, it is important to remark that in the frame of the present thesis, the lack of detection and identification techniques based in radar laser resulted in the need to research and provide more innovative approaches, based in the use of laser scanner, able to detect and identify the different actors involved in the road environment. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Los accidentes de tráfico son un grave problema social y económico, cada año el coste tanto en vidas humanas como económico es incontable, por lo que cualquier acción que conlleve la reducción o eliminación de esta lacra es importante. Durante los últimos años se han hecho avances para mitigar el número de accidentes y reducir sus consecuencias. Estos esfuerzos han dado sus frutos, reduciendo el número de accidentes y sus víctimas. Sin embargo el número de heridos y muertos en accidentes de este tipo es aún muy alto, por lo que no hay que rebajar los esfuerzos encaminados a hacer desaparecer tan importante problema. Los recientes avances en tecnologías de la información han permitido la creación de sistemas de ayuda a la conducción cada vez más complejos, capaces de ayudar e incluso sustituir al conductor, permitiendo una conducción más segura y eficiente. Pero estos complejos sistemas requieren de los sensores más fiables, capaces de permitir reconstruir el entorno, identificar los distintos objetos que se encuentran en él e identificar los potenciales peligros. Los sensores disponibles en la actualidad han demostrado ser insuficientes para tan ardua tarea, debido a los enormes requerimientos que conlleva una aplicación de seguridad en carretera. Por lo tanto, combinar los diferentes sensores disponibles se antoja necesario para llegar a los niveles de eficiencia y confianza que requieren este tipo de aplicaciones. De esta forma, las limitaciones de cada sensor pueden ser superadas, gracias al uso combinado de los diferentes sensores, cada uno de ellos proporcionando información que complementa la obtenida por otros sistemas. Este tipo de aplicaciones se denomina aplicaciones de Fusión Sensorial. El presente trabajo busca aportar soluciones en el entorno de los vehículos inteligentes, mediante técnicas de fusión sensorial, a clásicos problemas relacionados con la seguridad vial. Se buscará combinar diferentes sensores y otras fuentes de información, para obtener un sistema fiable, capaz de satisfacer las exigentes demandas de este tipo de aplicaciones. Los estudios realizados y algoritmos propuestos están enmarcados en dos campos de investigación bien conocidos y populares. Los Sistemas Inteligentes de Transporte (ITS- por sus siglas en ingles- Intelligent Transportation Systems), marco en el que se centra la presente tesis, que engloba las diferentes tecnologías que durante los últimos años han permitido dotar a los sistemas de transporte de mejoras que aumentan la seguridad y eficiencia de los sistemas de transporte tradicionales, gracias a las novedades en el campo de las tecnologías de la información. Por otro lado las técnicas de Fusión Sensorial (DF -por sus siglas en ingles- Data Fusión) engloban las diferentes técnicas y procesos necesarios para combinar diferentes fuentes de información, permitiendo mejorar las prestaciones y dando fiabilidad a los sistemas finales. La presente tesis buscará el empleo de las técnicas de Fusión Sensorial para dar solución a problemas relacionados con Sistemas Inteligentes de Transporte. Los sensores escogidos para esta aplicación son un escáner láser y visión por computador. El primero es un sensor ampliamente conocido, que durante los últimos años ha comenzado a emplearse en el mundo de los ITS con unos excelentes resultados. El segundo de este conjunto de sensores es uno de los sistemas más empleados durante los últimos años, para dotar de cada vez más complejos y versátiles aplicaciones en el mundo de los ITS. Gracias a la visión por computador, aplicaciones tan necesarias para la seguridad como detección de señales de tráfico, líneas de la carreta, peatones, etcétera, que hace unos años parecía ciencia ficción, están cada vez más cerca. La aplicación que se presenta pretende dar solución al problema de reconstrucción de entornos viales, identificando a los principales usuarios de la carretera (vehículos y peatones) mediante técnicas de Fusión Sensorial. La solución implementada busca dar una completa solución a todos los niveles del proceso de fusión sensorial, proveyendo de las diferentes herramientas, no solo para detectar los otros usuarios, sino para dar una estimación del peligro que cada una de estas detecciones implica. Para lograr este propósito, además de los sensores ya comentados han sido necesarias otras fuentes de información, como sensores GPS, inerciales e información contextual. Los algoritmos presentados pretenden ser un importante paso adelante en el mundo de los Sistemas Inteligentes de Transporte, proporcionando novedosos algoritmos basados en tecnologías de Fusión Sensorial que permitirán detectar y estimar el movimiento de los peatones y vehículos de forma fiable y robusta. Finalmente hay que remarcar que en el marco de la presente tesis, la falta de sistemas de detección e identificación de obstáculos basados en radar láser provocó la necesidad de implementar novedosos algoritmos que detectasen e identificasen, en la medida de lo posible y pese a las limitaciones de la tecnología, los diferentes obstáculos que se pueden encontrar en la carretera basándose en este sensor

Radar based discrete and continuous activity recognition for assisted living

In an era of digital transformation, there is an appetite for automating the monitoring process of motions and actions by individuals who are part of a society increasingly getting older on average. ”Activity recognition” is where sensors use motion information from participants who are wearing a wearable sensor or are in the field of view of a remote sensor which, coupled with machine learning algorithms, can automatically identify the movement or action the person is undertaking. Radar is a nascent sensor for this application, having been proposed in literature as an effective privacy-compliant sensor that can track movements of the body effectively. The methods of recording movements are separated into two types where ’Discrete’ movements provide an overview of a single activity within a fixed interval of time, while ’Continuous’ activities present sequences of activities performed in a series with variable duration and uncertain transitions, making these a challenging and yet much more realistic classification problem. In this thesis, first an overview of the technology of continuous wave (CW) and frequency modulated continuous wave (FMCW) radars and the machine learning algorithms and classification concepts is provided. Following this, state of the art for activity recognition with radar is presented and the key papers and significant works are discussed. The remaining chapters of this thesis discuss the research topics where contributions were made. This is commenced through analysing the effect of the physiology of the subject under test, to show that age can have an effect on the radar readings on the target. This is followed by porting existing radar recognition technologies and presenting novel use of radar based gait recognition to detect lameness in animals. Reverting to the human-centric application, improvements to activity recognition on humans and its accuracy was demonstrated by utilising features from different domains with feature selection and using different sensing technologies cooperatively. Finally, using a Bi-long short term memory (LSTM) based network, improved recognition of continuous activities and activity transitions without human-dependent feature extraction was demonstrated. Accuracy rate of 97% was achieved through sensor fusion and feature selection for discrete activities and for continuous activities, the Bi-LSTM achieved 92% accuracy with a sole radar sensor

Foveation for 3D visualization and stereo imaging

Even though computer vision and digital photogrammetry share a number of goals, techniques, and methods, the potential for cooperation between these fields is not fully exploited. In attempt to help bridging the two, this work brings a well-known computer vision and image processing technique called foveation and introduces it to photogrammetry, creating a hybrid application. The results may be beneficial for both fields, plus the general stereo imaging community, and virtual reality applications. Foveation is a biologically motivated image compression method that is often used for transmitting videos and images over networks. It is possible to view foveation as an area of interest management method as well as a compression technique. While the most common foveation applications are in 2D there are a number of binocular approaches as well. For this research, the current state of the art in the literature on level of detail, human visual system, stereoscopic perception, stereoscopic displays, 2D and 3D foveation, and digital photogrammetry were reviewed. After the review, a stereo-foveation model was constructed and an implementation was realized to demonstrate a proof of concept. The conceptual approach is treated as generic, while the implementation was conducted under certain limitations, which are documented in the relevant context. A stand-alone program called Foveaglyph is created in the implementation process. Foveaglyph takes a stereo pair as input and uses an image matching algorithm to find the parallax values. It then calculates the 3D coordinates for each pixel from the geometric relationships between the object and the camera configuration or via a parallax function. Once 3D coordinates are obtained, a 3D image pyramid is created. Then, using a distance dependent level of detail function, spherical volume rings with varying resolutions throughout the 3D space are created. The user determines the area of interest. The result of the application is a user controlled, highly compressed non-uniform 3D anaglyph image. 2D foveation is also provided as an option. This type of development in a photogrammetric visualization unit is beneficial for system performance. The research is particularly relevant for large displays and head mounted displays. Although, the implementation, because it is done for a single user, would possibly be best suited to a head mounted display (HMD) application. The resulting stereo-foveated image can be loaded moderately faster than the uniform original. Therefore, the program can potentially be adapted to an active vision system and manage the scene as the user glances around, given that an eye tracker determines where exactly the eyes accommodate. This exploration may also be extended to robotics and other robot vision applications. Additionally, it can also be used for attention management and the viewer can be directed to the object(s) of interest the demonstrator would like to present (e.g. in 3D cinema). Based on the literature, we also believe this approach should help resolve several problems associated with stereoscopic displays such as the accommodation convergence problem and diplopia. While the available literature provides some empirical evidence to support the usability and benefits of stereo foveation, further tests are needed. User surveys related to the human factors in using stereo foveated images, such as its possible contribution to prevent user discomfort and virtual simulator sickness (VSS) in virtual environments, are left as future work.reviewe

A Survey of Augmented Reality

© 2015 M. Billinghurst, A. Clark, and G. Lee. This survey summarizes almost 50 years of research and development in the field of Augmented Reality (AR). From early research in the 1960's until widespread availability by the 2010's there has been steady progress towards the goal of being able to seamlessly combine real and virtual worlds. We provide an overview of the common definitions of AR, and show how AR fits into taxonomies of other related technologies. A history of important milestones in Augmented Reality is followed by sections on the key enabling technologies of tracking, display and input devices. We also review design guidelines and provide some examples of successful AR applications. Finally, we conclude with a summary of directions for future work and a review of some of the areas that are currently being researched

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI)