Download or read book Urban Environment Perception and Navigation Using Robotic Vision written by Giovani Bernardes Vitor and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context,where the road layout may be very complex, the presence of objects such as trees, bicycles,cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to dea lwith uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully,understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement basedon decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, with out the need to adapt the infrastructure,without requiring previous knowledge of the environment and considering the presenceof dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and tofollow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensors.
Download or read book Robotic Localization and Perception in Static Terrain and Dynamic Urban Environments written by Isaac Thomas Miller and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents a complete, real-time, field-proven approach to robotic localization and perception for full-size field robots operating outdoors in static terrain and dynamic urban environments. The approach emphasizes formal probabilistic yet efficient frameworks for solving salient problems related to robotic localization and perception, including 1) estimating robot position, velocity, and attitude by fusing GNSS signals with onboard inertial and odometry sensors, 2) aiding these navigation solutions with measurements from onboard landmark sensors referencing a pre-surveyed map of environmental features, 3) estimating the locations and shapes of static terrain features around the robot, and 4) detecting and tracking the locations, shapes, and maneuvers of dynamic obstacles moving near the robot. The approach taken herein gives both theoretical and data-driven accounts of the localization and perception algorithms developed to solve these problems for Cornell University's 2005 DARPA Grand Challenge robot and 2007 DARPA Urban Challenge robot. The approach presented here is divided into four main components. The first component statistically evaluates variants of an Extended Square Root Information Filter fusing GNSS signals with onboard inertial and odometry sensors to estimate robot position, velocity, and attitude. The evaluation determines the filter's sensitivity to map-aiding, differential corrections, integrity monitoring, WAAS augmentation, carrier phases, and extensive signal black- outs. The second component presents the PosteriorPose algorithm, a particle filtering approach for augmenting robotic navigation solutions with vision-based measurements of nearby lanes and stop lines referenced against a known map. These measurements are shown to improve the quality of the navigation solution when GNSS signals are available, and they keep the navigation solution converged in extended signal blackouts. The third component presents a terrain estimation algorithm using Gaussian sum elevation densities to model terrain variations in a planar gridded elevation model. The algorithm is validated experimentally on the 2005 Cornell University DARPA Grand Challenge robot. The fourth component presents the LocalMap tracking algorithm, a real-time solution to the joint estimation problem of data assignment and dynamic obstacle tracking from a potentially moving robot. The algorithm is validated in controlled experiments with full-size vehicles, and on data collected at the 2007 DARPA Urban Challenge.
Download or read book Active Perception and Robot Vision written by Arun K. Sood and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 747 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent robotics has become the focus of extensive research activity. This effort has been motivated by the wide variety of applications that can benefit from the developments. These applications often involve mobile robots, multiple robots working and interacting in the same work area, and operations in hazardous environments like nuclear power plants. Applications in the consumer and service sectors are also attracting interest. These applications have highlighted the importance of performance, safety, reliability, and fault tolerance. This volume is a selection of papers from a NATO Advanced Study Institute held in July 1989 with a focus on active perception and robot vision. The papers deal with such issues as motion understanding, 3-D data analysis, error minimization, object and environment modeling, object detection and recognition, parallel and real-time vision, and data fusion. The paradigm underlying the papers is that robotic systems require repeated and hierarchical application of the perception-planning-action cycle. The primary focus of the papers is the perception part of the cycle. Issues related to complete implementations are also discussed.
Download or read book New Development in Robot Vision written by Yu Sun and published by Springer. This book was released on 2014-09-26 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of robotic vision has advanced dramatically recently with the development of new range sensors. Tremendous progress has been made resulting in significant impact on areas such as robotic navigation, scene/environment understanding, and visual learning. This edited book provides a solid and diversified reference source for some of the most recent important advancements in the field of robotic vision. The book starts with articles that describe new techniques to understand scenes from 2D/3D data such as estimation of planar structures, recognition of multiple objects in the scene using different kinds of features as well as their spatial and semantic relationships, generation of 3D object models, approach to recognize partially occluded objects, etc. Novel techniques are introduced to improve 3D perception accuracy with other sensors such as a gyroscope, positioning accuracy with a visual servoing based alignment strategy for microassembly, and increasing object recognition reliability using related manipulation motion models. For autonomous robot navigation, different vision-based localization and tracking strategies and algorithms are discussed. New approaches using probabilistic analysis for robot navigation, online learning of vision-based robot control, and 3D motion estimation via intensity differences from a monocular camera are described. This collection will be beneficial to graduate students, researchers, and professionals working in the area of robotic vision.
Download or read book Visual Perception for Humanoid Robots written by David Israel González Aguirre and published by Springer. This book was released on 2018-09-01 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of model-based environmental visual perception for humanoid robots. The visual perception of a humanoid robot creates a bidirectional bridge connecting sensor signals with internal representations of environmental objects. The objective of such perception systems is to answer two fundamental questions: What & where is it? To answer these questions using a sensor-to-representation bridge, coordinated processes are conducted to extract and exploit cues matching robot’s mental representations to physical entities. These include sensor & actuator modeling, calibration, filtering, and feature extraction for state estimation. This book discusses the following topics in depth: • Active Sensing: Robust probabilistic methods for optimal, high dynamic range image acquisition are suitable for use with inexpensive cameras. This enables ideal sensing in arbitrary environmental conditions encountered in human-centric spaces. The book quantitatively shows the importance of equipping robots with dependable visual sensing. • Feature Extraction & Recognition: Parameter-free, edge extraction methods based on structural graphs enable the representation of geometric primitives effectively and efficiently. This is done by eccentricity segmentation providing excellent recognition even on noisy & low-resolution images. Stereoscopic vision, Euclidean metric and graph-shape descriptors are shown to be powerful mechanisms for difficult recognition tasks. • Global Self-Localization & Depth Uncertainty Learning: Simultaneous feature matching for global localization and 6D self-pose estimation are addressed by a novel geometric and probabilistic concept using intersection of Gaussian spheres. The path from intuition to the closed-form optimal solution determining the robot location is described, including a supervised learning method for uncertainty depth modeling based on extensive ground-truth training data from a motion capture system. The methods and experiments are presented in self-contained chapters with comparisons and the state of the art. The algorithms were implemented and empirically evaluated on two humanoid robots: ARMAR III-A & B. The excellent robustness, performance and derived results received an award at the IEEE conference on humanoid robots and the contributions have been utilized for numerous visual manipulation tasks with demonstration at distinguished venues such as ICRA, CeBIT, IAS, and Automatica.
Download or read book Visual Navigation for Robots in Urban and Indoor Environments written by Yan Lu and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As a fundamental capability for mobile robots, navigation involves multiple tasks including localization, mapping, motion planning, and obstacle avoidance. In unknown environments, a robot has to construct a map of the environment while simultaneously keeping track of its own location within the map. This is known as simultaneous localization and mapping (SLAM). For urban and indoor environments, SLAM is especially important since GPS signals are often unavailable. Visual SLAM uses cameras as the primary sensor and is a highly attractive but challenging research topic. The major challenge lies in the robustness to lighting variation and uneven feature distribution. Another challenge is to build semantic maps composed of high-level landmarks. To meet these challenges, we investigate feature fusion approaches for visual SLAM. The basic rationale is that since urban and indoor environments contain various feature types such points and lines, in combination these features should improve the robustness, and meanwhile, high-level landmarks can be defined as or derived from these combinations. We design a novel data structure, multilayer feature graph (MFG), to organize five types of features and their inner geometric relationships. Building upon a two view-based MFG prototype, we extend the application of MFG to image sequence-based mapping by using EKF. We model and analyze how errors are generated and propagated through the construction of a two view-based MFG. This enables us to treat each MFG as an observation in the EKF update step. We apply the MFG-EKF method to a building exterior mapping task and demonstrate its efficacy. Two view based MFG requires sufficient baseline to be successfully constructed, which is not always feasible. Therefore, we further devise a multiple view based algorithm to construct MFG as a global map. Our proposed algorithm takes a video stream as input, initializes and iteratively updates MFG based on extracted key frames; it also refines robot localization and MFG landmarks using local bundle adjustment. We show the advantage of our method by comparing it with state-of-the-art methods on multiple indoor and outdoor datasets. To avoid the scale ambiguity in monocular vision, we investigate the application of RGB-D for SLAM.We propose an algorithm by fusing point and line features. We extract 3D points and lines from RGB-D data, analyze their measurement uncertainties, and compute camera motion using maximum likelihood estimation. We validate our method using both uncertainty analysis and physical experiments, where it outperforms the counterparts under both constant and varying lighting conditions. Besides visual SLAM, we also study specular object avoidance, which is a great challenge for range sensors. We propose a vision-based algorithm to detect planar mirrors. We derive geometric constraints for corresponding real-virtual features across images and employ RANSAC to develop a robust detection algorithm. Our algorithm achieves a detection accuracy of 91.0%. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155525
Download or read book Active Robot Vision Camera Heads Model Based Navigation And Reactive Control written by Kevin Bowyer and published by World Scientific. This book was released on 1993-05-13 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contents:Editorial (H I Christensen et al.)The Harvard Binocular Head (N J Ferrier & J J Clark)Heads, Eyes, and Head-Eye Systems (K Pahlavan & J-O Eklundh)Design and Performance of TRISH, a Binocular Robot Head with Torsional Eye Movements (E Milios et al.)A Low-Cost Robot Camera Head (H I Christensen)The Surrey Attentive Robot Vision System (J R G Pretlove & G A Parker)Layered Control of a Binocular Camera Head (J L Crowley et al.)SAVIC: A Simulation, Visualization and Interactive Control Environment for Mobile Robots (C Chen & M M Trivedi)Simulation and Expectation in Sensor-Based Systems (Y Roth & R Jain)Active Avoidance: Escape and Dodging Behaviors for Reactive Control (R C Arkin et al.) Readership: Engineers and computer scientists. keywords:Active Vision;Robot Vision;Computer Vision;Model-Based Vision;Robot Navigation;Reactive Control;Robot Motion Planning;Knowledge-Based Vision;Robotics
Download or read book Vision Based Autonomous Robot Navigation written by Amitava Chatterjee and published by Springer. This book was released on 2012-10-13 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is devoted to the theory and development of autonomous navigation of mobile robots using computer vision based sensing mechanism. The conventional robot navigation systems, utilizing traditional sensors like ultrasonic, IR, GPS, laser sensors etc., suffer several drawbacks related to either the physical limitations of the sensor or incur high cost. Vision sensing has emerged as a popular alternative where cameras can be used to reduce the overall cost, maintaining high degree of intelligence, flexibility and robustness. This book includes a detailed description of several new approaches for real life vision based autonomous navigation algorithms and SLAM. It presents the concept of how subgoal based goal-driven navigation can be carried out using vision sensing. The development concept of vision based robots for path/line tracking using fuzzy logic is presented, as well as how a low-cost robot can be indigenously developed in the laboratory with microcontroller based sensor systems. The book describes successful implementation of integration of low-cost, external peripherals, with off-the-shelf procured robots. An important highlight of the book is that it presents a detailed, step-by-step sample demonstration of how vision-based navigation modules can be actually implemented in real life, under 32-bit Windows environment. The book also discusses the concept of implementing vision based SLAM employing a two camera based system.
Download or read book Robotics Computer Vision and Intelligent Systems written by Péter Galambos and published by Springer Nature. This book was released on 2022-11-09 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the papers of two workshops which were held in conjunctionwith the First International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2020, Virtual Event, in November 4-6, 2020 and Second International Conference on Robotics, Computer Vision and Intelligent Systems,ROBOVIS 2021, Virtual Event, in October 25-27, 2021. The 11 revised full papers presented in this book were carefully reviewed and selectedfrom 53 submissions.
Download or read book Vision for Robotics written by Danica Kragic and published by Now Publishers Inc. This book was released on 2009 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot vision refers to the capability of a robot to visually perceive the environment and use this information for execution of various tasks. Visual feedback has been used extensively for robot navigation and obstacle avoidance. In the recent years, there are also examples that include interaction with people and manipulation of objects. In this paper, we review some of the work that goes beyond of using artificial landmarks and fiducial markers for the purpose of implementing visionbased control in robots. We discuss different application areas, both from the systems perspective and individual problems such as object tracking and recognition.
Download or read book Big Data Code and the Discrete City written by Silvio Carta and published by Routledge. This book was released on 2019-06-19 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data, Code and the Discrete City explores how digital technologies are gradually changing the way in which the public space is designed by architects, managed by policymakers and experienced by individuals. Smart city technologies are superseding the traditional human experience that has characterised the making of the public space until today. This book examines how computers see the public space and the effect of algorithms, artificial intelligences and automated processes on the human experience in public spaces. Divided into three parts, the first part of this book examines the notion of discreteness in its origins and applications to computer sciences. The second section presents a dual perspective: it explores the ways in which public spaces are constructed by the computer-driven logic and then translated into control mechanisms, design strategies and software-aided design. This perspective also describes the way in which individuals perceive this new public space, through its digital logic, and discrete mechanisms (from Wi-Fi coverage to self-tracking). Finally, in the third part, this book scrutinises the discrete logic with which computers operate, and how this is permeating into aspects of city life. This book is valuable for anyone interested in urban studies and digital technologies, and more specifically in big data, urban informatics and public space.
Download or read book Robotic Vision Technologies for Machine Learning and Vision Applications written by Garcia-Rodriguez, Jose and published by IGI Global. This book was released on 2012-12-31 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic systems consist of object or scene recognition, vision-based motion control, vision-based mapping, and dense range sensing, and are used for identification and navigation. As these computer vision and robotic connections continue to develop, the benefits of vision technology including savings, improved quality, reliability, safety, and productivity are revealed. Robotic Vision: Technologies for Machine Learning and Vision Applications is a comprehensive collection which highlights a solid framework for understanding existing work and planning future research. This book includes current research on the fields of robotics, machine vision, image processing and pattern recognition that is important to applying machine vision methods in the real world.
Download or read book Sensor based Navigation Applied to Intelligent Electric Vehicles written by Danilo Alves de Lima and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous navigation of car-like robots is a large domain with several techniques and applications working in cooperation. It ranges from low-level control to global navigation, passing by environment perception, robot localization, and many others in asensor-based approach. Although there are very advanced works, they still presenting problems and limitations related to the environment where the car is inserted and the sensors used. This work addresses the navigation problem of car-like robots based on low cost sensors in urban environments. For this purpose, an intelligent electric vehicle was equipped with vision cameras and other sensors to be applied in three big areas of robot navigation : the Environment Perception, Local Navigation Control, and Global Navigation Management. In the environment perception, a 2D and 3D image processing approach was proposed to segment the road area and detect the obstacles. This segmentation approach also provides some image features to local navigation control.Based on the previous detected information, a hybrid control approach for vision based navigation with obstacle avoidance was applied to road lane following. It is composed by the validation of a Visual Servoing methodology (deliberative controller) in a new Image-based Dynamic Window Approach (reactive controller). To assure the car's global navigation, we proposed the association of the data from digital maps in order tomanage the local navigation at critical points, like road intersections. Experiments in a challenging scenario with both simulated and real experimental car show the viabilityof the proposed methodology.
Download or read book Handling Uncertainty and Networked Structure in Robot Control written by Lucian Bușoniu and published by Springer. This book was released on 2016-02-06 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on two challenges posed in robot control by the increasing adoption of robots in the everyday human environment: uncertainty and networked communication. Part I of the book describes learning control to address environmental uncertainty. Part II discusses state estimation, active sensing, and complex scenario perception to tackle sensing uncertainty. Part III completes the book with control of networked robots and multi-robot teams. Each chapter features in-depth technical coverage and case studies highlighting the applicability of the techniques, with real robots or in simulation. Platforms include mobile ground, aerial, and underwater robots, as well as humanoid robots and robot arms. Source code and experimental data are available at http://extras.springer.com. The text gathers contributions from academic and industry experts, and offers a valuable resource for researchers or graduate students in robot control and perception. It also benefits researchers in related areas, such as computer vision, nonlinear and learning control, and multi-agent systems.
Download or read book Artificial Vision for Mobile Robots written by Nicholas Ayache and published by MIT Press. This book was released on 1991 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: To give mobile robots real autonomy, and to permit them to act efficiently in a diverse, cluttered, and changing environment, they must be equipped with powerful tools for perception and reasoning. Artificial Vision for Mobile Robots presents new theoretical and practical tools useful for providing mobile robots with artificial vision in three dimensions, including passive binocular and trinocular stereo vision, local and global 3D map reconstructions, fusion of local 3D maps into a global 3D map, 3D navigation, control of uncertainty, and strategies of perception. Numerous examples from research carried out at INRIA with the Esprit Depth and Motion Analysis project are presented in a clear and concise manner. Nicolas Ayache is Research Director at INRIA, Le Chesnay, France. Contents. General Introduction. Stereo Vision. Introduction. Calibration. Image Representation. Binocular Stereo Vision Constraints. Binocular Stereo Vision Algorithms. Experiments in Binocular Stereo Vision. Trinocular Stereo Vision, Outlook. Multisensory Perception. Introduction. A Unified Formalism. Geometric Representation. Construction of Visual Maps. Combining Visual Maps. Results: Matching and Motion. Results: Matching and Fusion. Outlook.
Download or read book Visual Navigation written by John Aloimonos and published by Psychology Press. This book was released on 1997 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: All biological systems with vision move about their environments and successfully perform many tasks. The same capabilities are needed in the world of robots. To that end, recent results in empirical fields that study insects and primates, as well as in theoretical and applied disciplines that design robots, have uncovered a number of the principles of navigation. To offer a unifying approach to the situation, this book brings together ideas from zoology, psychology, neurobiology, mathematics, geometry, computer science, and engineering. It contains theoretical developments that will be essential in future research on the topic -- especially new representations of space with less complexity than Euclidean representations possess. These representations allow biological and artificial systems to compute from images in order to successfully deal with their environments. In this book, the barriers between different disciplines have been smoothed and the workings of vision systems of biological organisms are made clear in computational terms to computer scientists and engineers. At the same time, fundamental principles arising from computational considerations are made clear both to empirical scientists and engineers. Empiricists can generate a number of hypotheses that they could then study through various experiments. Engineers can gain insight for designing robotic systems that perceive aspects of their environment. For the first time, readers will find: * the insect vision system presented in a way that can be understood by computational scientists working in computer vision and engineering; * three complete, working robotic navigation systems presented with all the issues related to their design analyzed in detail; * the beginning of a computational theory of direct perception, as advocated by Gibson, presented in detail with applications for a variety of problems; and * the idea that vision systems could compute space representations different from perfect metric descriptions -- and be used in robotic tasks -- advanced for both artificial and biological systems.
Download or read book Environmental Perception Technology for Unmanned Systems written by Xin Bi and published by Springer Nature. This book was released on 2020-09-30 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the principles and technology of environmental perception in unmanned systems. With the rapid development of a new generation of information technologies such as automatic control and information perception, a new generation of robots and unmanned systems will also take on new importance. This book first reviews the development of autonomous systems and subsequently introduces readers to the technical characteristics and main technologies of the sensor. Lastly, it addresses aspects including autonomous path planning, intelligent perception and autonomous control technology under uncertain conditions. For the first time, the book systematically introduces the core technology of autonomous system information perception.
