Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

The Future of the Operating Room: Surgical Preplanning and Navigation using High Accuracy Ultra-Wideband Positioning and Advanced Bone Measurement

This dissertation embodies the diversity and creativity of my research, of which much has been peer-reviewed, published in archival quality journals, and presented nationally and internationally. Portions of the work described herein have been published in the fields of image processing, forensic anthropology, physical anthropology, biomedical engineering, clinical orthopedics, and microwave engineering. The problem studied is primarily that of developing the tools and technologies for a next-generation surgical navigation system. The discussion focuses on the underlying technologies of a novel microwave positioning subsystem and a bone analysis subsystem. The methodologies behind each of these technologies are presented in the context of the overall system with the salient results helping to elucidate the difficult facets of the problem. The microwave positioning system is currently the highest accuracy wireless ultra-wideband positioning system that can be found in the literature. The challenges in producing a system with these capabilities are many, and the research and development in solving these problems should further the art of high accuracy pulse-based positioning

Real Virtuality: A Code of Ethical Conduct. Recommendations for Good Scientific Practice and the Consumers of VR-Technology

The goal of this article is to present a first list of ethical concerns that may arise from research and personal use of virtual reality (VR) and related technology, and to offer concrete recommendations for minimizing those risks. Many of the recommendations call for focused research initiatives. In the first part of the article, we discuss the relevant evidence from psychology that motivates our concerns. In Section “Plasticity in the Human Mind,” we cover some of the main results suggesting that one’s environment can influence one’s psychological states, as well as recent work on inducing illusions of embodiment. Then, in Section “Illusions of Embodiment and Their Lasting Effect,” we go on to discuss recent evidence indicating that immersion in VR can have psychological effects that last after leaving the virtual environment. In the second part of the article, we turn to the risks and recommendations. We begin, in Section “The Research Ethics of VR,” with the research ethics of VR, covering six main topics: the limits of experimental environments, informed consent, clinical risks, dual-use, online research, and a general point about the limitations of a code of conduct for research. Then, in Section “Risks for Individuals and Society,” we turn to the risks of VR for the general public, covering four main topics: long-term immersion, neglect of the social and physical environment, risky content, and privacy. We offer concrete recommendations for each of these 10 topics, summarized in Table 1

Antenas setoriais para sistemas de localização em redes de sensores sem fios

Doutoramento em Engenharia EletrotécnicaThis work investigates low cost localization systems (LS) based on received signal strength (RSS) and integrated with different types of antennas with main emphasis on sectorial antennas. The last few years have witnessed an outstanding growth in wireless sensor networks (WSN). Among its various possible applications, the localization field became a major area of research. The localization techniques based on RSS are characterized by simplicity and low cost of integration. The integration of LS based on RSS and sectorial antennas (SA) was proven to provide an effective solution for reducing the number of required nodes of the networks and allows the combination of several techniques, such as RSS and angle of arrival (AoA). This PhD thesis focuses on studying techniques, antennas and protocols that best meet the needs of each LS with main focus on low cost systems based on RSS and AoA. Firstly there are studied localization techniques and system that best suit the requirements of the user and the antennas that are most appropriate according to the nature of the signal. In this step it is intended to provide a fundamental understanding of the undertaken work. Then the developed antennas are presented according to the following categories: sectorial and microstrip antennas. Two sectorial antennas are presented: a narrowband antenna operating at 2.4 to 2.5 GHz and a broadband antenna operating at 800MHz-2.4GHz. The low cost printed antennas were designed to operate at 5 GHz, which may be used for vehicular communication. After presenting the various antennas, several prototypes of indoor/outdoor LS are implemented and analyzed. Localization protocols are also proposed, one based on simplicity and low power, and the other on interoperability with different types of antennas and system requirements.O presente trabalho investiga sistemas de localização (SL) de baixo custo baseados na intensidade do sinal (RSS) e integrados com diferentes tipos de antenas com principal destaque para antenas sectoriais. Os últimos anos testemunharam um crescimento surpreendente de redes de sensores sem fios (RSSF), onde entre diversas aplicações possíveis, a localização tornou-se uma das principais áreas de pesquisa. Técnicas baseadas na intensidade do sinal caracterizam-se pela simplicidade e baixo custo de integração. A integração de SL baseados na intensidade do sinal recebido e antenas sectoriais (AS) oferecem uma solução eficaz para reduzir o número de nós necessários e para combinar diversas técnicas de localização. Esta tese de doutoramento foca-se no estudado de técnicas, antenas e protocolos de acordo com os requisitos de cada sistema localização com especial atenção para sistemas de baixo custo baseados na intensidade do sinal e no ângulo de chegada. Inicialmente são estudadas técnicas e SL de acordo com as necessidades do utilizador e as antenas que melhor se enquadram de acordo com a natureza do sinal. Esta etapa tem como objectivo proporcionar a compreensão fundamental do trabalho desenvolvido. Em seguida são apresentadas as antenas desenvolvidas divididas em: antenas sectorias e antenas impressas de baixo custo. Duas antenas sectoriais são apresentadas: uma de banda estreita a operar a 2,4-2,5GHz e outro de banda larga 800MHz-2.4GHz. As antenas impressas foram desenvolvidas para operar a 5 GHz, pelo que podem ser utilizadas para comunicação veicular. Após apresentação das diversas antenas vários protótipos de SL interiores/exteriores são implementados e analisados. Protocolos de localização são também propostos, um baseado na simplicidade e baixo consumo, outro na interoperabilidade com diferentes tipos de antenas e requisitos do sistema

On unifying sparsity and geometry for image-based 3D scene representation

Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providing a satisfying, immersive experience. Camera networks offer a low cost solution to the acquisition of 3D visual information, by capturing multi-view images from different viewpoints. However, the camera's representation of the data is not ideal for common tasks such as data compression or 3D scene analysis, as it does not make the 3D scene geometry explicit. Image-based scene representations fundamentally require a multi-view image model that facilitates extraction of underlying geometrical relationships between the cameras and scene components. Developing new, efficient multi-view image models is thus one of the major challenges in image-based 3D scene representation methods. This dissertation focuses on defining and exploiting a new method for multi-view image representation, from which the 3D geometry information is easily extractable, and which is additionally highly compressible. The method is based on sparse image representation using an overcomplete dictionary of geometric features, where a single image is represented as a linear combination of few fundamental image structure features (edges for example). We construct the dictionary by applying a unitary operator to an analytic function, which introduces a composition of geometric transforms (translations, rotation and anisotropic scaling) to that function. The advantage of this approach is that the features across multiple views can be related with a single composition of transforms. We then establish a connection between image components and scene geometry by defining the transforms that satisfy the multi-view geometry constraint, and obtain a new geometric multi-view correlation model. We first address the construction of dictionaries for images acquired by omnidirectional cameras, which are particularly convenient for scene representation due to their wide field of view. Since most omnidirectional images can be uniquely mapped to spherical images, we form a dictionary by applying motions on the sphere, rotations, and anisotropic scaling to a function that lives on the sphere. We have used this dictionary and a sparse approximation algorithm, Matching Pursuit, for compression of omnidirectional images, and additionally for coding 3D objects represented as spherical signals. Both methods offer better rate-distortion performance than state of the art schemes at low bit rates. The novel multi-view representation method and the dictionary on the sphere are then exploited for the design of a distributed coding method for multi-view omnidirectional images. In a distributed scenario, cameras compress acquired images without communicating with each other. Using a reliable model of correlation between views, distributed coding can achieve higher compression ratios than independent compression of each image. However, the lack of a proper model has been an obstacle for distributed coding in camera networks for many years. We propose to use our geometric correlation model for distributed multi-view image coding with side information. The encoder employs a coset coding strategy, developed by dictionary partitioning based on atom shape similarity and multi-view geometry constraints. Our method results in significant rate savings compared to independent coding. An additional contribution of the proposed correlation model is that it gives information about the scene geometry, leading to a new camera pose estimation method using an extremely small amount of data from each camera. Finally, we develop a method for learning stereo visual dictionaries based on the new multi-view image model. Although dictionary learning for still images has received a lot of attention recently, dictionary learning for stereo images has been investigated only sparingly. Our method maximizes the likelihood that a set of natural stereo images is efficiently represented with selected stereo dictionaries, where the multi-view geometry constraint is included in the probabilistic modeling. Experimental results demonstrate that including the geometric constraints in learning leads to stereo dictionaries that give both better distributed stereo matching and approximation properties than randomly selected dictionaries. We show that learning dictionaries for optimal scene representation based on the novel correlation model improves the camera pose estimation and that it can be beneficial for distributed coding

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Fingers micro-gesture recognition based on holoscopic 3D imaging system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonMicro-gesture recognition has been widely research in recent years, in particular there has been a great focus on 3D micro-gesture recognition which consists of classifying the micro-gesture movements of the fingers for touch-less control applications. Holoscopic 3D imaging system mimics fly’s eye technique to capture true 3D scene which is enrich in both texture and motion information. As a result, holoscopic 3D imaging system shall be a suitable approach for robust recognition application. This PhD research focuses on innovative 3D micro-gesture recognition based on holoscopic 3D system which delivers robust and reliable performance with precision for 3D micro-gestures. Indeed this can be applied to other wide range of applications such as Internet of things (IoT), AR/VR, robotics and other touch-less interaction. Due to lack of holoscopic 3D dataset, a comprehensive 3D micro-gesture dataset (HoMG) includes both holoscopic 3D images and videos is prepared. It is a reasonable size holoscopic 3D dataset which is captured with different camera settings and conditions from 40 participants. Innovative 3D micro-gesture recognition is proposed based on 2D feature extraction methods with basic classification methods, the recognition accuracy can reach around 50.9%. For video-based data, the 3D feature extraction methods are achieved 66.7% recognition accuracy over 50.9% accuracy for micro-gesture images as the initial investigation. HoMG database held a challenge in IEEE International automatic face and gesture 2018, and 4 groups from the international research institutes joined the challenge and contributed many new methods as further development where the proposed method was published. The holoscopic 3D dataset further enrich innovative micro-gesture 3D recognition system is proposed and its performance is evaluated by carrying out like to like comparison with state of the art methods. In addition, a fast and efficient pre-processing algorithm for H3D images to extract the element images. Simplified viewpoint image extraction method are presented. A pre-trained CNN model with the attention mechanics is implemented based on VP image for the predicted probabilities of gesture. The proposed approached is further improved using voting strategy. The proposed approach achieves 87% accuracy, which outperform all existing state of the art methods on the image-based database. Advanced 3D micro-gesture recognition is investigated based on sequence video database, the end-to-end model has been used on effective H3D based micro-gesture recognition system. For front-end network, there are two method of traditional viewpoint image extraction and novel pseudo viewpoint image extraction have been used and evaluated. The pseudo viewpoint (PVP) front-end has been created, which used to deep learning networks understanding the implied 3D information of H3D imaging system. The viewpoint (VP) front-end follows the traditional H3D image method to extract and reconstruct the multi-viewpoint images. Both front-end have been feed in four popular advanced deep networks using for learning and classification. This experiments evaluated the performance of 2D/3D convolutional, mixing 2D and 3D convolutional and LSTM on the HoMG video database, which is beneficial to H3D imaging system using deep learning network. Finally, in order to obtain the high accuracies, the majority voting has been applied for further improve. The final results show that the performance is not only better than the traditional methods, but also superior to the existing deep learning based approaches, which clearly demonstrates the effectiveness of the proposed approach