Application Of Multi Sensor Fusion For Cascade Landmark Recognition And Vehicle Localization For Autonomous Driving
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Application Of Multi Sensor Fusion For Cascade Landmark Recognition And Vehicle Localization For Autonomous Driving
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Author : 王昱翔
language : en
Publisher:
Release Date : 2020
Application Of Multi Sensor Fusion For Cascade Landmark Recognition And Vehicle Localization For Autonomous Driving written by 王昱翔 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Design And Analysis Of Modern Tracking Systems
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Author : Samuel S. Blackman
language : en
Publisher: Artech House Publishers
Release Date : 1999
Design And Analysis Of Modern Tracking Systems written by Samuel S. Blackman and has been published by Artech House Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Technology & Engineering categories.
Here's a thorough overview of the state-of-the-art in design and implementation of advanced tracking for single and multiple sensor systems. This practical resource provides modern system designers and analysts with in-depth evaluations of sensor management, kinematic and attribute data processing, data association, situation assessment, and modern tracking and data fusion methods as applied in both military and non-military arenas.
Introduction To Autonomous Mobile Robots Second Edition
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Author : Roland Siegwart
language : en
Publisher: MIT Press
Release Date : 2011-02-18
Introduction To Autonomous Mobile Robots Second Edition written by Roland Siegwart and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-18 with Computers categories.
The second edition of a comprehensive introduction to all aspects of mobile robotics, from algorithms to mechanisms. Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques. This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners. Curriculum developed by Dr. Robert King, Colorado School of Mines, and Dr. James Conrad, University of North Carolina-Charlotte, to accompany the National Instruments LabVIEW Robotics Starter Kit, are available. Included are 13 (6 by Dr. King and 7 by Dr. Conrad) laboratory exercises for using the LabVIEW Robotics Starter Kit to teach mobile robotics concepts.
Autonomous Driving
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Author : Markus Maurer
language : en
Publisher: Springer
Release Date : 2016-05-21
Autonomous Driving written by Markus Maurer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-21 with Technology & Engineering categories.
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".
Multi Sensor Fusion For Autonomous Driving
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Author : Xinyu Zhang
language : en
Publisher: Springer Nature
Release Date : 2023-08-28
Multi Sensor Fusion For Autonomous Driving written by Xinyu Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-28 with Technology & Engineering categories.
Although sensor fusion is an essential prerequisite for autonomous driving, it entails a number of challenges and potential risks. For example, the commonly used deep fusion networks are lacking in interpretability and robustness. To address these fundamental issues, this book introduces the mechanism of deep fusion models from the perspective of uncertainty and models the initial risks in order to create a robust fusion architecture. This book reviews the multi-sensor data fusion methods applied in autonomous driving, and the main body is divided into three parts: Basic, Method, and Advance. Starting from the mechanism of data fusion, it comprehensively reviews the development of automatic perception technology and data fusion technology, and gives a comprehensive overview of various perception tasks based on multimodal data fusion. The book then proposes a series of innovative algorithms for various autonomous driving perception tasks, to effectively improve the accuracy and robustness of autonomous driving-related tasks, and provide ideas for solving the challenges in multi-sensor fusion methods. Furthermore, to transition from technical research to intelligent connected collaboration applications, it proposes a series of exploratory contents such as practical fusion datasets, vehicle-road collaboration, and fusion mechanisms. In contrast to the existing literature on data fusion and autonomous driving, this book focuses more on the deep fusion method for perception-related tasks, emphasizes the theoretical explanation of the fusion method, and fully considers the relevant scenarios in engineering practice. Helping readers acquire an in-depth understanding of fusion methods and theories in autonomous driving, it can be used as a textbook for graduate students and scholars in related fields or as a reference guide for engineers who wish to apply deep fusion methods.
Application Of Multi Sensor Fusion In Autonomous Vehicle Localization Under Sensor Anomalies
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Author :
language : en
Publisher:
Release Date : 2021
Application Of Multi Sensor Fusion In Autonomous Vehicle Localization Under Sensor Anomalies written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Sensor Fusion In Localization Mapping And Tracking
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Author : Constantin Wellhausen
language : en
Publisher:
Release Date : 2024
Sensor Fusion In Localization Mapping And Tracking written by Constantin Wellhausen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Making autonomous driving possible requires extensive information about the surroundings as well as the state of the vehicle. While specific information can be obtained through singular sensors, a full estimation requires a multi sensory approach, including redundant sources of information to increase robustness. This thesis gives an overview of tasks that arise in sensor fusion in autonomous driving, and presents solutions at a high level of detail, including derivations and parameters where required to enable re-implementation. The thesis includes theoretical considerations of the approaches as well as practical evaluations. Evaluations are also included for approaches that did not prove to solve their tasks robustly. This follows the belief that both results further the state of the art by giving researchers ideas about suitable and unsuitable approaches, where otherwise the unsuitable approaches may be re-implemented multiple times with similar results. The thesis focuses on model-based methods, also referred to in the following as classical methods, with a special focus on probabilistic and evidential theories. Methods based on deep learning are explicitly not covered to maintain explainability and robustness which would otherwise strongly rely on the available training data. The main focus of the work lies in three main fields of autonomous driving: localization, which estimates the state of the ego-vehicle, mapping or obstacle detection, where drivable areas are identified, and object detection and tracking, which estimates the state of all surrounding traffic participants. All algorithms are designed with the requirements of autonomous driving in mind, with a focus on robustness, real-time capability and usability of the approaches in all potential scenarios that may arise in urban driving. In localization the state of the vehicle is determined. While traditionally global positioning systems such as a Global Navigation Satellite System (GNSS) are often used for this task, they are prone to errors and may produce jumps in the position estimate which may cause unexpected and dangerous behavior. The focus of research in this thesis is the development of a localization system which produces a smooth state estimate without any jumps. For this two localization approaches are developed and executed in parallel. One localization is performed without global information to avoid jumps. This however only provides odometry, which drifts over time and does not give global positioning. To provide this information the second localization includes GNSS information, thus providing a global estimate which is free of global drift. Additionally the use of LiDAR odometry for improving the localization accuracy is evaluated. For mapping the focus of this thesis is on providing a computationally efficient mapping system which is capable of being used in arbitrarily large areas with no predefined size. This is achieved by mapping only the direct environment of the vehicle, with older information in the map being discarded. This is motivated by the observation that the environment in autonomous driving is highly dynamic and must be mapped anew every time the vehicles sensors observe an area. The provided map gives subsequent algorithms information about areas where the vehicle can or cannot drive. For this an occupancy grid map is used, which discretizes the map into cells of a fixed size, with each cell estimating whether its corresponding space in the world is occupied. However the grid map is not created for the entire area which could potentially be visited, as this may be very large and potentially impossible to represent in the working memory. Instead the map is created only for a window around the vehicle, with the vehicle roughly in the center. A hierarchical map organization is used to allow efficient moving of the window as the vehicle moves through an area. For the hierarchical map different data structures are evaluated for their time and space complexity in order to find the most suitable implementation for the presented mapping approach. Finally for tracking a late-fusion approach to the multi-sensor fusion task of estimating states of all other traffic participants is presented. Object detections are obtained from LiDAR, camera and Radar sensors, with an additional source of information being obtained from vehicle-to-everything communication which is also fused in the late fusion. The late fusion is developed for easy extendability and with arbitrary object detection algorithms in mind. For the first evaluation it relies on black box object detections provided by the sensors. In the second part of the research in object tracking multiple algorithms for object detection on LiDAR data are evaluated for the use in the object tracking framework to ease the reliance on black box implementations. A focus is set on detecting objects from motion, where three different approaches are evaluated for motion estimation in LiDAR data: LiDAR optical flow, evidential dynamic mapping and normal distribution transforms. The thesis contains both theoretical contributions and practical implementation considerations for the presented approaches with a high degree of detail including all necessary derivations. All results are implemented and evaluated on an autonomous vehicle and real-world data. With the developed algorithms autonomous driving is realized for urban areas.
Sensor Fusion For 3d Object Detection For Autonomous Vehicles
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Author : Yahya Massoud
language : en
Publisher:
Release Date : 2021
Sensor Fusion For 3d Object Detection For Autonomous Vehicles written by Yahya Massoud and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Thanks to the major advancements in hardware and computational power, sensor technology, and artificial intelligence, the race for fully autonomous driving systems is heating up. With a countless number of challenging conditions and driving scenarios, researchers are tackling the most challenging problems in driverless cars. One of the most critical components is the perception module, which enables an autonomous vehicle to "see" and "understand" its surrounding environment. Given that modern vehicles can have large number of sensors and available data streams, this thesis presents a deep learning-based framework that leverages multimodal data - i.e. sensor fusion, to perform the task of 3D object detection and localization. We provide an extensive review of the advancements of deep learning-based methods in computer vision, specifically in 2D and 3D object detection tasks. We also study the progress of the literature in both single-sensor and multi-sensor data fusion techniques. Furthermore, we present an in-depth explanation of our proposed approach that performs sensor fusion using input streams from LiDAR and Camera sensors, aiming to simultaneously perform 2D, 3D, and Bird's Eye View detection. Our experiments highlight the importance of learnable data fusion mechanisms and multi-task learning, the impact of different CNN design decisions, speed-accuracy tradeoffs, and ways to deal with overfitting in multi-sensor data fusion frameworks.
Deep Learning For Sensor Fusion
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Author : Shaun Michael Howard
language : en
Publisher:
Release Date : 2017
Deep Learning For Sensor Fusion written by Shaun Michael Howard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Automotive sensors categories.
The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).
Automatic Laser Calibration Mapping And Localization For Autonomous Vehicles
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Author : Jesse Sol Levinson
language : en
Publisher: Stanford University
Release Date : 2011
Automatic Laser Calibration Mapping And Localization For Autonomous Vehicles written by Jesse Sol Levinson and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
This dissertation presents several related algorithms that enable important capabilities for self-driving vehicles. Using a rotating multi-beam laser rangefinder to sense the world, our vehicle scans millions of 3D points every second. Calibrating these sensors plays a crucial role in accurate perception, but manual calibration is unreasonably tedious, and generally inaccurate. As an alternative, we present an unsupervised algorithm for automatically calibrating both the intrinsics and extrinsics of the laser unit from only seconds of driving in an arbitrary and unknown environment. We show that the results are not only vastly easier to obtain than traditional calibration techniques, they are also more accurate. A second key challenge in autonomous navigation is reliable localization in the face of uncertainty. Using our calibrated sensors, we obtain high resolution infrared reflectivity readings of the world. From these, we build large-scale self-consistent probabilistic laser maps of urban scenes, and show that we can reliably localize a vehicle against these maps to within centimeters, even in dynamic environments, by fusing noisy GPS and IMU readings with the laser in realtime. We also present a localization algorithm that was used in the DARPA Urban Challenge, which operated without a prerecorded laser map, and allowed our vehicle to complete the entire six-hour course without a single localization failure. Finally, we present a collection of algorithms for the mapping and detection of traffic lights in realtime. These methods use a combination of computer-vision techniques and probabilistic approaches to incorporating uncertainty in order to allow our vehicle to reliably ascertain the state of traffic-light-controlled intersections.