Multi-sensor Fusion for Autonomous Driving
Author: Xinyu Zhang
Publisher: Springer Nature
Published:
Total Pages: 237
ISBN-13: 9819932807
DOWNLOAD EBOOKRead and Download eBook Full
Author: Xinyu Zhang
Publisher: Springer Nature
Published:
Total Pages: 237
ISBN-13: 9819932807
DOWNLOAD EBOOKAuthor: Giacomo Della Riccia
Publisher: Springer
Published: 2014-05-04
Total Pages: 252
ISBN-13: 3709125804
DOWNLOAD EBOOKThis work is a collection of front-end research papers on data fusion and perceptions. Authors are leading European experts of Artificial Intelligence, Mathematical Statistics and/or Machine Learning. Area overlaps with "Intelligent Data Analysis”, which aims to unscramble latent structures in collected data: Statistical Learning, Model Selection, Information Fusion, Soccer Robots, Fuzzy Quantifiers, Emotions and Artifacts.
Author: Jonathan Robert Schoenberg
Publisher:
Published: 2012
Total Pages: 342
ISBN-13:
DOWNLOAD EBOOKThis thesis explores data fusion and distributed robotic perception through a series of theoretical developments, analyses and experiments. First, a GSF with component extended Kalman filters (EKF) is proposed as an approach to localize an autonomous vehicle in an urban environment with limited GPS availability. The GSF is used because of its ability to represent the posterior distribution of the vehicle pose with better efficiency (fewer terms, less computational complexity) than a corresponding bootstrap particle filter with various numbers of particles due to the interaction with measurement hypothesis tests. A series of in-depth empirical studies are performed using 37 minutes of recorded data from Cornell University's autonomous vehicle driven in an urban environment, including a 32 minute GPS blackout. Second, a distributed grid-based terrain mapping algorithm using Gaussian Mixture Models is developed for use in tree connected and arbitrary connected sensor networks. The distributed data fusion rules are developed that operates directly on the sufficient statistics summarizing the grid-cell height and uncertainty. The distributed grid-based terrain mapping algorithms is demonstrated in an experimental environment involving 8 autonomous robots operating in an indoor environment for 120 seconds. Third, an algorithm to segment 3D points in dense range maps generated from the fusion of a single optical camera and a multiple emitter/detector laser range finder is presented. The algorithm is demonstrated on data collected with the Cornell University DARPA Urban Challenge vehicle. Finally, two information theoretic procedures for fusing multiple distributions with unknown correlation are developed. The first approach developed is Entropy Weighted Chernoff fusion; this fusion procedure biases the WEP fusion weight towards the distribution with the lowest entropy. An information loss for the WEP conservative fusion rule is introduced and an approximation derived by computing the Kullback-Leibler divergence between the Naive Bayes and WEP fused distributions. The approximation is minimized for the second fusion approach: Minimum-Information-Loss fusion; the procedure generates the least conservative fused distribution in the family of WEP results. Experimental results include the fusion of multiple occupancy grid maps over an optimally connected sensor network, demonstrating consistent map estimates.
Author: David Hall
Publisher: CRC Press
Published: 2017-12-19
Total Pages: 501
ISBN-13: 1351833057
DOWNLOAD EBOOKWith the recent proliferation of service-oriented architectures (SOA), cloud computing technologies, and distributed-interconnected systems, distributed fusion is taking on a larger role in a variety of applications—from environmental monitoring and crisis management to intelligent buildings and defense. Drawing on the work of leading experts around the world, Distributed Data Fusion for Network-Centric Operations examines the state of the art of data fusion in a distributed sensing, communications, and computing environment. Get Insight into Designing and Implementing Data Fusion in a Distributed Network Addressing the entirety of information fusion, the contributors cover everything from signal and image processing, through estimation, to situation awareness. In particular, the work offers a timely look at the issues and solutions involving fusion within a distributed network enterprise. These include critical design problems, such as how to maintain a pedigree of agents or nodes that receive information, provide their contribution to the dataset, and pass to other network components. The book also tackles dynamic data sharing within a network-centric enterprise, distributed fusion effects on state estimation, graph-theoretic methods to optimize fusion performance, human engineering factors, and computer ontologies for higher levels of situation assessment. A comprehensive introduction to this emerging field and its challenges, the book explores how data fusion can be used within grid, distributed, and cloud computing architectures. Bringing together both theoretical and applied research perspectives, this is a valuable reference for fusion researchers and practitioners. It offers guidance and insight for those working on the complex issues of designing and implementing distributed, decentralized information fusion.
Author: Hassen Fourati
Publisher: CRC Press
Published: 2017-12-19
Total Pages: 639
ISBN-13: 1482263750
DOWNLOAD EBOOKMultisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors Describes new materials and achievements in optimal fusion and multisensor filters Discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies Explores the topology, communication structure, computational resources, fusion level, goals, and optimization of multisensor data fusion system architectures Showcases applications of multisensor data fusion in fields such as medicine, transportation's traffic, defense, and navigation Multisensor Data Fusion: From Algorithms and Architectural Design to Applications is a robust collection of modern multisensor data fusion methodologies. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution.
Author: Richard T. Antony
Publisher: Artech House
Published: 2015-11-01
Total Pages: 367
ISBN-13: 1608078469
DOWNLOAD EBOOKThis new resource provides a coherent, intuitive, and theoretical foundation for the fusion and exploitation of traditional sensor data as well as text-based information. In addition to presenting a detailed discussion of base-level data fusion requirements, a variety of higher level exploitation algorithms are presented that perform fully automated relationship discovery, rank interest level of entities, and support context-sensitive behavior understanding (both static and dynamic context). This book identifies eight canonical fusion forms as well as twenty foundational fusion services to enable formal mapping between models and services. Normalization and representation processes for (hard) sensor data and (soft) semantic data are described as well as methods for combining hard and soft data. Included is a prototype fusion system developed to implement virtually all the presented applications in order to demonstrate the robustness and utility of the design principles presented in this resource. The prototype system presented supports a variety of user workflows and all the applications are fully integrated. There is extensive fusion system output for unclassified scenarios to permit the reader to fully understand all presented design principles. This book also presents context-sensitive fuzzy semantic spatial and temporal reasoning.
Author:
Publisher:
Published: 2002
Total Pages: 48
ISBN-13: 9789176683200
DOWNLOAD EBOOKAuthor: Marina Cocchi
Publisher: Elsevier
Published: 2019-05-11
Total Pages: 396
ISBN-13: 0444639853
DOWNLOAD EBOOKData Fusion Methodology and Applications explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales. Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery Includes comprehensible, theoretical chapters written for large and diverse audiences Provides a wealth of selected application to the topics included
Author: Bruno Siciliano
Publisher: Springer
Published: 2016-07-27
Total Pages: 2259
ISBN-13: 3319325523
DOWNLOAD EBOOKThe second edition of this handbook provides a state-of-the-art overview on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people and their lives. The credible prospect of practical robots among humans is the result of the scientific endeavour of a half a century of robotic developments that established robotics as a modern scientific discipline. The ongoing vibrant expansion and strong growth of the field during the last decade has fueled this second edition of the Springer Handbook of Robotics. The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organization’s Award for Engineering & Technology. The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook’s team. A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app. Springer Handbook of Robotics Multimedia Extension Portal: http://handbookofrobotics.org/
Author: H B Mitchell
Publisher: Springer Science & Business Media
Published: 2012-02-09
Total Pages: 349
ISBN-13: 3642272223
DOWNLOAD EBOOKThis textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion. It is an extensively revised second edition of the author's successful book: "Multi-Sensor Data Fusion: An Introduction" which was originally published by Springer-Verlag in 2007. The main changes in the new book are: New Material: Apart from one new chapter there are approximately 30 new sections, 50 new examples and 100 new references. At the same time, material which is out-of-date has been eliminated and the remaining text has been rewritten for added clarity. Altogether, the new book is nearly 70 pages longer than the original book. Matlab code: Where appropriate we have given details of Matlab code which may be downloaded from the worldwide web. In a few places, where such code is not readily available, we have included Matlab code in the body of the text. Layout. The layout and typography has been revised. Examples and Matlab code now appear on a gray background for easy identification and advancd material is marked with an asterisk. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familarity with the basic tools of linear algebra, calculus and simple probability is recommended. Although conceptually simple, the study of mult-sensor data fusion presents challenges that are unique within the education of the electrical engineer or computer scientist. To become competent in the field the student must become familiar with tools taken from a wide range of diverse subjects including: neural networks, signal processing, statistical estimation, tracking algorithms, computer vision and control theory. All too often, the student views multi-sensor data fusion as a miscellaneous assortment of different processes which bear no relationship to each other. In contrast, in this book the processes are unified by using a common statistical framework. As a consequence, the underlying pattern of relationships that exists between the different methodologies is made evident. The book is illustrated with many real-life examples taken from a diverse range of applications and contains an extensive list of modern references.