588 research outputs found

    Self-supervised Vector-Quantization in Visual SLAM using Deep Convolutional Autoencoders

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    In this paper, we introduce AE-FABMAP, a new self-supervised bag of words-based SLAM method. We also present AE-ORB-SLAM, a modified version of the current state of the art BoW-based path planning algorithm. That is, we have used a deep convolutional autoencoder to find loop closures. In the context of bag of words visual SLAM, vector quantization (VQ) is considered as the most time-consuming part of the SLAM procedure, which is usually performed in the offline phase of the SLAM algorithm using unsupervised algorithms such as Kmeans++. We have addressed the loop closure detection part of the BoW-based SLAM methods in a self-supervised manner, by integrating an autoencoder for doing vector quantization. This approach can increase the accuracy of large-scale SLAM, where plenty of unlabeled data is available. The main advantage of using a self-supervised is that it can help reducing the amount of labeling. Furthermore, experiments show that autoencoders are far more efficient than semi-supervised methods like graph convolutional neural networks, in terms of speed and memory consumption. We integrated this method into the state of the art long range appearance based visual bag of word SLAM, FABMAP2, also in ORB-SLAM. Experiments demonstrate the superiority of this approach in indoor and outdoor datasets over regular FABMAP2 in all cases, and it achieves higher accuracy in loop closure detection and trajectory generation

    Semi-supervised Vector-Quantization in Visual SLAM using HGCN

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    In this paper, two semi-supervised appearance based loop closure detection technique, HGCN-FABMAP and HGCN-BoW are introduced. Furthermore an extension to the current state of the art localization SLAM algorithm, ORB-SLAM, is presented. The proposed HGCN-FABMAP method is implemented in an off-line manner incorporating Bayesian probabilistic schema for loop detection decision making. Specifically, we let a Hyperbolic Graph Convolutional Neural Network (HGCN) to operate over the SURF features graph space, and perform vector quantization part of the SLAM procedure. This part previously was performed in an unsupervised manner using algorithms like HKmeans, kmeans++,..etc. The main Advantage of using HGCN, is that it scales linearly in number of graph edges. Experimental results shows that HGCN-FABMAP algorithm needs far more cluster centroids than HGCN-ORB, otherwise it fails to detect loop closures. Therefore we consider HGCN-ORB to be more efficient in terms of memory consumption, also we conclude the superiority of HGCN-BoW and HGCN-FABMAP with respect to other algorithms

    Loop Closure Detection Algorithm Based on Greedy Strategy for Visual SLAM

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    动态环境与视觉混淆严重影响视觉闭环检测性能.基于贪心策略,提出了一种在线构建视觉词典的闭环检测算法.算法优先处理Surf描述与已有单词Surf描述欧式距离最大的特征点,改进特征点与单词Surf描述最近邻的约束条件,生成了表征性能强、量化误差小的视觉词典,算法具备实时性,并在动态环境图像集与视觉混淆多发生的图像集上,在确保100%,准确率的条件下,最大召回率分别提升了5%,与4%,.The performance of loop closure detection is seriously affected by dynamic objects and perceptual aliasing in the environment. Based on greedy strategy, a real-time loop closure detection approach using online visual dictionary is proposed. The process of dictionary construction gives priority to dealing with Surf feature that has the maximum Euclidean distance from the closest vocabulary word. A more discriminative and representative visual vocabulary is produced through adding constraint condition to the nearest neighbor distance. This visual vocabulary guarantees a small quantization error. The proposed approach meets real-time constraints. Experiments based on datasets from dynamic environments and visually repetitive environments demonstrated that the largest recall rate increased by 5% and 4% respectively at 100% precision. © 2017, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.国家自然科学基金资助项目(61274133). Supported by the National Natural Science Foundation of China(61274133

    Loop Closure Detection Algorithm Based on Greedy Strategy for Visual SLAM

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    动态环境与视觉混淆严重影响视觉闭环检测性能.基于贪心策略,提出了一种在线构建视觉词典的闭环检测算法.算法优先处理Surf描述与已有单词Surf描; 述欧式距离最大的特征点,改进特征点与单词Surf描述最近邻的约束条件,生成了表征性能强、量化误差小的视觉词典,算法具备实时性,并在动态环境图像集; 与视觉混淆多发生的图像集上,在确保100%,准确率的条件下,最大召回率分别提升了5%,与4%,.The performance of loop closure detection is seriously affected by; dynamic objects and perceptual aliasing in the environment.Based on; greedy strategy,a real-time loop closure detection approach using online; visual dictionary is proposed.The process of dictionary construction; gives priority to dealing with Surf feature that has the maximum; Euclidean distance from the closest vocabulary word.A more; discriminative and representative visual vocabulary is produced through; adding constraint condition to the nearest neighbor distance.This visual; vocabulary guarantees a small quantization error.The proposed approach; meets real-time constraints.Experiments based on datasets from dynamic; environments and visually repetitive environments demonstrated that the; largest recall rate increased by 5%, and 4%, respectively at 100%,; precision.国家自然科学基金资助项

    How Technology Evolution and Disruption are Defining the World’s Entrepreneurial Ecosystems: The Case of Barcelona’s Startup Ecosystem

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    This article provides a critical overview of the development process of entrepreneurial ecosystems and the role played by technology and startups within such process. The analysis focus on the characteristics and components of entrepreneurial ecosystems with special attention to startups, as they are the main actors of these ecosystems. The objectives are reached through a critical literature review. Results show the evolution of these ecosystems, and an in-deep analysis of the role played by startups, big companies and governments in such evolution. The knowledge paradox between universities and startups is also taken into account together with and the importance of cities in the development of successful entrepreneurial ecosystems. We apply the result of our critical review to the analysis of the case of the Barcelona Ecosystem. Last section is devoted to policy implications for the strengthening of entrepreneurial ecosystems with special reference to the universities and the need for a redesign of technology transfer strategies. Success factors analysis and specific policy recommendations can help to a better understanding and policy planning of entrepreneurial ecosystems

    Topological place recognition for life-long visual localization

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    Premio Extraordinario de Doctorado de la UAH en el año académico 2016-2017La navegación de vehículos inteligentes o robots móviles en períodos largos de tiempo ha experimentado un gran interés por parte de la comunidad investigadora en los últimos años. Los sistemas basados en cámaras se han extendido ampliamente en el pasado reciente gracias a las mejoras en sus características, precio y reducción de tamaño, añadidos a los progresos en técnicas de visión artificial. Por ello, la localización basada en visión es una aspecto clave para desarrollar una navegación autónoma robusta en situaciones a largo plazo. Teniendo en cuenta esto, la identificación de localizaciones por medio de técnicas de reconocimiento de lugar topológicas puede ser complementaria a otros enfoques como son las soluciones basadas en el Global Positioning System (GPS), o incluso suplementaria cuando la señal GPS no está disponible.El estado del arte en reconocimiento de lugar topológico ha mostrado un funcionamiento satisfactorio en el corto plazo. Sin embargo, la localización visual a largo plazo es problemática debido a los grandes cambios de apariencia que un lugar sufre como consecuencia de elementos dinámicos, la iluminación o la climatología, entre otros. El objetivo de esta tesis es enfrentarse a las dificultades de llevar a cabo una localización topológica eficiente y robusta a lo largo del tiempo. En consecuencia, se van a contribuir dos nuevos enfoques basados en reconocimiento visual de lugar para resolver los diferentes problemas asociados a una localización visual a largo plazo. Por un lado, un método de reconocimiento de lugar visual basado en descriptores binarios es propuesto. La innovación de este enfoque reside en la descripción global de secuencias de imágenes como códigos binarios, que son extraídos mediante un descriptor basado en la técnica denominada Local Difference Binary (LDB). Los descriptores son eficientemente asociados usando la distancia de Hamming y un método de búsqueda conocido como Approximate Nearest Neighbors (ANN). Además, una técnica de iluminación invariante es aplicada para mejorar el funcionamiento en condiciones luminosas cambiantes. El empleo de la descripción binaria previamente introducida proporciona una reducción de los costes computacionales y de memoria.Por otro lado, también se presenta un método de reconocimiento de lugar visual basado en deep learning, en el cual los descriptores aplicados son procesados por una Convolutional Neural Network (CNN). Este es un concepto recientemente popularizado en visión artificial que ha obtenido resultados impresionantes en problemas de clasificación de imagen. La novedad de nuestro enfoque reside en la fusión de la información de imagen de múltiples capas convolucionales a varios niveles y granularidades. Además, los datos redundantes de los descriptores basados en CNNs son comprimidos en un número reducido de bits para una localización más eficiente. El descriptor final es condensado aplicando técnicas de compresión y binarización para realizar una asociación usando de nuevo la distancia de Hamming. En términos generales, los métodos centrados en CNNs mejoran la precisión generando representaciones visuales de las localizaciones más detalladas, pero son más costosos en términos de computación.Ambos enfoques de reconocimiento de lugar visual son extensamente evaluados sobre varios datasets públicos. Estas pruebas arrojan una precisión satisfactoria en situaciones a largo plazo, como es corroborado por los resultados mostrados, que comparan nuestros métodos contra los principales algoritmos del estado del arte, mostrando mejores resultados para todos los casos.Además, también se ha analizado la aplicabilidad de nuestro reconocimiento de lugar topológico en diferentes problemas de localización. Estas aplicaciones incluyen la detección de cierres de lazo basada en los lugares reconocidos o la corrección de la deriva acumulada en odometría visual usando la información proporcionada por los cierres de lazo. Asimismo, también se consideran las aplicaciones de la detección de cambios geométricos a lo largo de las estaciones del año, que son esenciales para las actualizaciones de los mapas en sistemas de conducción autónomos centrados en una operación a largo plazo. Todas estas contribuciones son discutidas al final de la tesis, incluyendo varias conclusiones sobre el trabajo presentado y líneas de investigación futuras

    The COVID-19 Pandemic and the Future of Working Spaces

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    This edited volume presents a compendium of emerging and innovative studies on the proliferation of new working spaces (NeWSps), both formal and informal (such as coworking spaces, maker spaces, fab labs, public libraries, and cofee shops), and their role during and following the COVID-19 pandemic in urban and regional development and planning. This book presents an original, interdisciplinary approach to NeWSps through three features: (i) situating the debate in the context of the COVID-19 pandemic, which has transformed NeWSp business models and the everyday work life of their owners and users; (ii) repositioning and rethinking the debate on NeWSps in the context of socioeconomics and planning and comparing conditions between before and during the COVID-19 pandemic; and (iii) providing new directions for urban and regional development and resilience to the COVID-19 pandemic, considering new ways of working and living. The 17 chapters are co-authored by both leading international scholars who have studied the proliferation of NeWSps in the last decade and young, talented researchers, resulting in a total of 55 co-authors from diferent disciplines (48 of whom are currently involved in the COST Action CA18214 ‘The Geography of New Working Spaces and Impact on the Periphery’ 2019–2023: www.newworking- spaces.eu). Selected comparative studies among several European countries (Western and Eastern Europe) and from the US and Lebanon are presented. The book contributes to the understanding of multi-disciplinary theoretical and practical implications of NeWSps for our society, economy, and urban/regional planning in conditions following the COVID-19 pandemic

    The COVID-19 Pandemic and the Future of Working Spaces

    Get PDF
    This edited volume presents a compendium of emerging and innovative studies on the proliferation of new working spaces (NeWSps), both formal and informal (such as coworking spaces, maker spaces, fab labs, public libraries, and coffee shops), and their role during and following the COVID-19 pandemic in urban and regional development and planning. This book presents an original, interdisciplinary approach to NeWSps through three features: (i) situating the debate in the context of the COVID-19 pandemic, which has transformed NeWSp business models and the everyday work life of their owners and users; (ii) repositioning and rethinking the debate on NeWSps in the context of socioeconomics and planning and comparing conditions between before and during the COVID-19 pandemic; and (iii) providing new directions for urban and regional development and resilience to the COVID-19 pandemic, considering new ways of working and living. The 17 chapters are co-authored by both leading international scholars who have studied the proliferation of NeWSps in the last decade and young, talented researchers, resulting in a total of 55 co-authors from different disciplines (48 of whom are currently involved in the COST Action CA18214 ‘The Geography of New Working Spaces and Impact on the Periphery’ 2019–2023: www.new-working-spaces.eu). Selected comparative studies among several European countries (Western and Eastern Europe) and from the US and Lebanon are presented. The book contributes to the understanding of multi-disciplinary theoretical and practical implications of NeWSps for our society, economy, and urban/regional planning in conditions following the COVID-19 pandemic

    Towards Our Common Digital Future. Flagship Report.

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    In the report “Towards Our Common Digital Future”, the WBGU makes it clear that sustainability strategies and concepts need to be fundamentally further developed in the age of digitalization. Only if digital change and the Transformation towards Sustainability are synchronized can we succeed in advancing climate and Earth-system protection and in making social progress in human development. Without formative political action, digital change will further accelerate resource and energy consumption, and exacerbate damage to the environment and the climate. It is therefore an urgent political task to create the conditions needed to place digitalization at the service of sustainable development
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