Large-Scale Simultaneous Localization and Mapping, B?dkowski

Автор: Niko S?nderhauf
Название: Switchable Constraints for Robust Simultaneous Localization and Mapping and Satellite-Based Localization
ISBN: 3031240154 ISBN-13(EAN): 9783031240157
Издательство: Springer
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Цена: 130430.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Simultaneous Localization and Mapping (SLAM) has been a long-standing research problem in robotics. It describes the problem of a robot mapping an unknown environment, while simultaneously localizing in it with the help of the incomplete map. This book describes a technique called Switchable Constraints.Switchable Constraints help to increase the robustness of SLAM against data association errors and in particular against false positive loop closure detections.

Such false positive loop closure detections can occur when the robot erroneously assumes it re-observed a landmark it has already mapped or when the appearance of the observed surroundings is very similar to the appearance of other places in the map. Ambiguous observations and appearances are very common in human-made environments such as office floors or suburban streets, making robustness against spurious observations a key challenge in SLAM. The book summarizes the foundations of factor graph-based SLAM techniques.

It explains the problem of data association errors before introducing the novel idea of Switchable Constraints. We present a mathematical derivation and probabilistic interpretation of Switchable Constraints along with evaluations on different datasets. The book shows that Switchable Constraints are applicable beyond SLAM problems and demonstrates the efficacy of this technique to improve the quality of satellite-based localization in urban environments, where multipath and non-line-of-sight situations are common error sources.

Автор: Janusz Bedkowski
Название: Large-Scale Simultaneous Localization and Mapping
ISBN: 9811919712 ISBN-13(EAN): 9789811919718
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book is dedicated for engineers and researchers who would like to increase the knowledge in area of mobile mapping systems.

Автор: Andreas N?chter
Название: 3D Robotic Mapping
ISBN: 3642100589 ISBN-13(EAN): 9783642100581
Издательство: Springer
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Цена: 130590.00 T
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Описание: This thorough treatment of 3D robotic mapping focuses on acquiring spatial models of physical environments through mobile robots. In it, new solutions to the 6D SLAM problem for 3D laser scans are proposed and a wide variety of applications are presented.

Автор: Juan Andrade Cetto; Alberto Sanfeliu
Название: Environment Learning for Indoor Mobile Robots
ISBN: 3642069312 ISBN-13(EAN): 9783642069314
Издательство: Springer
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Цена: 113180.00 T
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Описание: This monograph covers theoretical aspects of simultaneous localization and map building for mobile robots. One of the most relevant topics covered in this monograph is the theoretical formalism of partial observability in SLAM.

Автор: Pfanne
Название: In-Hand Object Localization and Control: Enabling Dexterous Manipulation with Robotic Hands
ISBN: 3031069668 ISBN-13(EAN): 9783031069666
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book introduces a novel model-based dexterous manipulation framework, which, thanks to its precision and versatility, significantly advances the capabilities of robotic hands compared to the previous state of the art. This is achieved by combining a novel grasp state estimation algorithm, the first to integrate information from tactile sensing, proprioception and vision, with an impedance-based in-hand object controller, which enables leading manipulation capabilities, including finger gaiting. The developed concept is implemented on one of the most advanced robotic manipulators, the DLR humanoid robot David, and evaluated in a range of challenging real-world manipulation scenarios and tasks. This book greatly benefits researchers in the field of robotics that study robotic hands and dexterous manipulation topics, as well as developers and engineers working on industrial automation applications involving grippers and robotic manipulators.

Автор: Yue Yufeng, Wang Danwei
Название: Collaborative Perception, Localization and Mapping for Autonomous Systems
ISBN: 9811588627 ISBN-13(EAN): 9789811588624
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book presents the breakthrough and cutting-edge progress for collaborative perception and mapping by proposing a novel framework of multimodal perception-relative localization-collaborative mapping for collaborative robot systems.

Автор: Yue Yufeng, Wang Danwei
Название: Collaborative Perception, Localization and Mapping for Autonomous Systems
ISBN: 9811588597 ISBN-13(EAN): 9789811588594
Издательство: Springer
Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the breakthrough and cutting-edge progress for collaborative perception and mapping by proposing a novel framework of multimodal perception-relative localization-collaborative mapping for collaborative robot systems.

Автор: Michael John Milford
Название: Robot Navigation from Nature
ISBN: 3642096263 ISBN-13(EAN): 9783642096266
Издательство: Springer
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Цена: 130590.00 T
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Описание: This pioneering book describes the development of a robot mapping and navigation system inspired by models of the neural mechanisms underlying spatial navigation in the rodent hippocampus. This is the first research to test existing models of rodent spatial mapping and navigation on robots in large, challenging, real world environments.

Автор: Mahdi Rezaei; Reinhard Klette
Название: Computer Vision for Driver Assistance
ISBN: 3319844261 ISBN-13(EAN): 9783319844268
Издательство: Springer
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Цена: 121110.00 T
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Автор: David Israel Gonz?lez Aguirre
Название: Visual Perception for Humanoid Robots
ISBN: 331997839X ISBN-13(EAN): 9783319978390
Издательство: Springer
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Цена: 93160.00 T
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Описание: 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.

Автор: David Israel Gonz?lez Aguirre
Название: Visual Perception for Humanoid Robots
ISBN: 3030074153 ISBN-13(EAN): 9783030074159
Издательство: Springer
Рейтинг:
Цена: 93160.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Автор: Yan Jing, Zhao Haiyan, Meng Yuan
Название: Localization in Underwater Sensor Networks
ISBN: 9811648301 ISBN-13(EAN): 9789811648304
Издательство: Springer
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Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: However, the openness and weak communication characteristics of USNs make underwater localization much more challenging in comparison to terrestrial sensor networks. In this book, we focus on the localization problem in USNs, taking into account the unique characteristics of the underwater environment.