Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment PDF Download

Author: Xiaochun Wang
Publisher: Springer
ISBN: 981139217X
Category : Technology & Engineering
Languages : en
Pages : 328

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Book Description
This book advances research on mobile robot localization in unknown environments by focusing on machine-learning-based natural scene recognition. The respective chapters highlight the latest developments in vision-based machine perception and machine learning research for localization applications, and cover such topics as: image-segmentation-based visual perceptual grouping for the efficient identification of objects composing unknown environments; classification-based rapid object recognition for the semantic analysis of natural scenes in unknown environments; the present understanding of the Prefrontal Cortex working memory mechanism and its biological processes for human-like localization; and the application of this present understanding to improve mobile robot localization. The book also features a perspective on bridging the gap between feature representations and decision-making using reinforcement learning, laying the groundwork for future advances in mobile robot navigation research.

Author: N. Subhashini
Publisher: Springer Nature
ISBN: 9811997489
Category : Technology & Engineering
Languages : en
Pages : 505

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Book Description
This book presents select proceedings of the Virtual International Conference on Futuristic Communication and Network Technologies (VICFCNT 2021). It covers various domains in communication engineering and networking technologies. This volume comprises recent research in areas like cyber-physical systems, acoustics, speech & video signal Processing, and the Internet of Things. This book is a collated work of academicians, researchers, and industry personnel from the international arena. This book will be useful for researchers, professionals, and engineers working in the core areas of electronics and communication.

Author: Min Song
Publisher: Springer Nature
ISBN: 3030590658
Category : Computers
Languages : en
Pages : 413

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Book Description
The volume LNCS 12393 constitutes the papers of the 22nd International Conference Big Data Analytics and Knowledge Discovery which will be held online in September 2020. The 15 full papers presented together with 14 short papers plus 1 position paper in this volume were carefully reviewed and selected from a total of 77 submissions. This volume offers a wide range to following subjects on theoretical and practical aspects of big data analytics and knowledge discovery as a new generation of big data repository, data pre-processing, data mining, text mining, sequences, graph mining, and parallel processing.

Author: Soon-Geul Lee
Publisher: Springer Nature
ISBN: 3031448510
Category :
Languages : en
Pages : 639

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Author: Leszek J. Chmielewski
Publisher: Springer
ISBN: 3030006921
Category : Computers
Languages : en
Pages : 536

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Book Description
This book constitutes the refereed proceedings of the International Conference on Computer Vision and Graphics, ICCVG 2018, held in Warsaw, Poland, in September 2018. The 45 full papers were selected from 117 submissions. The contributions are thematically arranged as follows: computer graphics, image quality and graphic, user interfaces, object classification and features, 3D and stereo image processing, low-level and middle-level image processing, medical image analysis, motion analysis and tracking, security and protection, pattern recognition and new concepts in classification.

Author: Mark Joseph DeClue
Publisher:
ISBN:
Category :
Languages : en
Pages : 191

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Book Description
The problem addressed in this research was to provide a capability for sensing previously unknown rectilinear, polyhedral-shaped objects in the operating environment of the autonomous mobile robot Yamabico-11. The approach to the system design was based on the application of edge extraction and least squares line fitting algorithms of PET92 to real-time camera images with subsequent filtering based on the environmental model of STE92. The output of this processing was employed in the recognition of obstacles and the determination of object range and dimensions. These measurements were then used in path tracking commands, supported by Yamabico's Model-based Mobile Robot Language (MML), for performing smooth, safe obstacle avoidance maneuvers. This work resulted in a system able to localize objects in images taken from the robot, provide location and size data, and cause proper path adjustments. Accuracies on the order of one to ten centimeters in range and one-half to two centimeters in dimensions were achieved.

Author: Garcia-Rodriguez, Jose
Publisher: IGI Global
ISBN: 1466627034
Category : Technology & Engineering
Languages : en
Pages : 535

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Book Description
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.

Author: Sebastian Thrun
Publisher: MIT Press
ISBN: 0262201623
Category : Technology & Engineering
Languages : en
Pages : 668

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Book Description
An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Author: Lu Fang
Publisher: Springer Nature
ISBN: 3030930467
Category : Computers
Languages : en
Pages : 815

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Book Description
This two-volume set LNCS 13069-13070 constitutes selected papers presented at the First CAAI International Conference on Artificial Intelligence, held in Hangzhou, China, in June 2021. Due to the COVID-19 pandemic the conference was partially held online. The 105 papers were thoroughly reviewed and selected from 307 qualified submissions. The papers are organized in topical sections on applications of AI; computer vision; data mining; explainability, understandability, and verifiability of AI; machine learning; natural language processing; robotics; and other AI related topics.

Author: Ahmad Hasasneh
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Usually, human beings are able to quickly distinguish between different places, solely from their visual appearance. This is due to the fact that they can organize their space as composed of discrete units. These units, called ``semantic places'', are characterized by their spatial extend and their functional unity. Such a semantic category can thus be used as contextual information which fosters object detection and recognition. Recent works in semantic place recognition seek to endow the robot with similar capabilities. Contrary to classical localization and mapping works, this problem is usually addressed as a supervised learning problem. The question of semantic places recognition in robotics - the ability to recognize the semantic category of a place to which scene belongs to - is therefore a major requirement for the future of autonomous robotics. It is indeed required for an autonomous service robot to be able to recognize the environment in which it lives and to easily learn the organization of this environment in order to operate and interact successfully. To achieve that goal, different methods have been already proposed, some based on the identification of objects as a prerequisite to the recognition of the scenes, and some based on a direct description of the scene characteristics. If we make the hypothesis that objects are more easily recognized when the scene in which they appear is identified, the second approach seems more suitable. It is however strongly dependent on the nature of the image descriptors used, usually empirically derived from general considerations on image coding.Compared to these many proposals, another approach of image coding, based on a more theoretical point of view, has emerged the last few years. Energy-based models of feature extraction based on the principle of minimizing the energy of some function according to the quality of the reconstruction of the image has lead to the Restricted Boltzmann Machines (RBMs) able to code an image as the superposition of a limited number of features taken from a larger alphabet. It has also been shown that this process can be repeated in a deep architecture, leading to a sparse and efficient representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. This approach has been successfully applied to the identification of tiny images from the 80 millions image database of the MIT. In the present work, we demonstrate that semantic place recognition can be achieved on the basis of tiny images instead of conventional Bag-of-Word (BoW) methods and on the use of Deep Belief Networks (DBNs) for image coding. We show that after appropriate coding a softmax regression in the projection space is sufficient to achieve promising classification results. To our knowledge, this approach has not yet been investigated for scene recognition in autonomous robotics. We compare our methods with the state-of-the-art algorithms using a standard database of robot localization. We study the influence of system parameters and compare different conditions on the same dataset. These experiments show that our proposed model, while being very simple, leads to state-of-the-art results on a semantic place recognition task.