Robust Localization and Mapping for Mobile Robotic Navigation: Theory, Algorithm and Implementation - Brossura
Sinossi
How can mobile robot navigate autonomously without the knowledge of its own state of pose and the model of the surrounding environment? Simultaneous Localization and Mapping (SLAM) is the mobile robot's significant and critical perceptual capability to achieve autonomous navigation, and it is applied in a wide range of application fields. Graph-based SLAM constructs a hierarchical topological graph to address the issues of localization and mapping, where the local relative spatial displacement of roto-translation and the global trajectory of the mobile robot are estimated, respectively. Then each local map can be transformed and integrated to the unique global frame for the construction of the global map. The theory, algorithm, and implementation of Graph-SLAM are explained in detail in this book. Scan matching is performed to estimate the local relative roto-translation, batch optimization is executed to estimate the global mobile robot's trajectory, and the global line-feature-based mapping is applied to construct the global line-feature model of the environment. The results in the experiments will verify the effectiveness of the proposed graph-based SLAM algorithm.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
Jingchun YIN, after graduation in Mechanical Eng. and Automation from Qingdao University (P.R.China) in 2007, he obtained Master's degree in Robotics from State Key Laboratory of Robotics and System, Harbin Institute of Technology (P.R.China) in 2010 , and Ph.D. degree in Robotics from Polytechnic University of Turin (Italy) in 2014.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -How can mobile robot navigate autonomously without the knowledge of its own state of pose and the model of the surrounding environment Simultaneous Localization and Mapping (SLAM) is the mobile robot's significant and critical perceptual capability to achieve autonomous navigation, and it is applied in a wide range of application fields. Graph-based SLAM constructs a hierarchical topological graph to address the issues of localization and mapping, where the local relative spatial displacement of roto-translation and the global trajectory of the mobile robot are estimated, respectively. Then each local map can be transformed and integrated to the unique global frame for the construction of the global map. The theory, algorithm, and implementation of Graph-SLAM are explained in detail in this book. Scan matching is performed to estimate the local relative roto-translation, batch optimization is executed to estimate the global mobile robot's trajectory, and the global line-feature-based mapping is applied to construct the global line-feature model of the environment. The results in the experiments will verify the effectiveness of the proposed graph-based SLAM algorithm. 132 pp. Englisch. Codice articolo 9783838360119
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yin JingchunJingchun YIN, after graduation in Mechanical Eng. and Automation from Qingdao University (P.R.China) in 2007, he obtained Master s degree in Robotics from State Key Laboratory of Robotics and System, Harbin Institute of T. Codice articolo 5416368
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -How can mobile robot navigate autonomously without the knowledge of its own state of pose and the model of the surrounding environment Simultaneous Localization and Mapping (SLAM) is the mobile robot's significant and critical perceptual capability to achieve autonomous navigation, and it is applied in a wide range of application fields. Graph-based SLAM constructs a hierarchical topological graph to address the issues of localization and mapping, where the local relative spatial displacement of roto-translation and the global trajectory of the mobile robot are estimated, respectively. Then each local map can be transformed and integrated to the unique global frame for the construction of the global map. The theory, algorithm, and implementation of Graph-SLAM are explained in detail in this book. Scan matching is performed to estimate the local relative roto-translation, batch optimization is executed to estimate the global mobile robot's trajectory, and the global line-feature-based mapping is applied to construct the global line-feature model of the environment. The results in the experiments will verify the effectiveness of the proposed graph-based SLAM algorithm.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch. Codice articolo 9783838360119
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