Robotic Mapping and Exploration [electronic resource] / by Cyrill Stachniss.

By: Stachniss, Cyrill [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Springer Tracts in Advanced Robotics: 55Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009Description: XVIII, 198 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642010972Subject(s): Engineering | Artificial intelligence | Systems theory | Control engineering systems | Engineering | Control , Robotics, Mechatronics | Artificial Intelligence (incl. Robotics) | Systems Theory, ControlAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 LOC classification: TJ210.2-211.495TJ163.12Online resources: Click here to access online
Contents:
Basic Techniques -- I: Exploration with Known Poses -- Decision-Theoretic Exploration Using Coverage Maps -- Coordinated Multi-Robot Exploration -- Multi-Robot Exploration Using Semantic Place Labels -- II: Mapping and Exploration under Pose Uncertainty -- Efficient Techniques for Rao-Blackwellized Mapping -- Actively Closing Loops During Exploration -- Recovering Particle Diversity -- Information Gain-based Exploration -- Mapping and Localization in Non-Static Environments -- Conclusion.
In: Springer eBooksSummary: "Robotic Mapping and Exploration" is an important contribution in the area of simultaneous localization and mapping (SLAM) for autonomous robots, which has been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the autonomous mapping learning problem. Solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. Results are accompanied by a rich set of experiments, revealing a promising outlook toward the application to a wide range of mobile robots and field settings, such as search and rescue, transportation tasks, or automated vacuum cleaning.
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Basic Techniques -- I: Exploration with Known Poses -- Decision-Theoretic Exploration Using Coverage Maps -- Coordinated Multi-Robot Exploration -- Multi-Robot Exploration Using Semantic Place Labels -- II: Mapping and Exploration under Pose Uncertainty -- Efficient Techniques for Rao-Blackwellized Mapping -- Actively Closing Loops During Exploration -- Recovering Particle Diversity -- Information Gain-based Exploration -- Mapping and Localization in Non-Static Environments -- Conclusion.

"Robotic Mapping and Exploration" is an important contribution in the area of simultaneous localization and mapping (SLAM) for autonomous robots, which has been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the autonomous mapping learning problem. Solutions include uncertainty-driven exploration, active loop closing, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments. Results are accompanied by a rich set of experiments, revealing a promising outlook toward the application to a wide range of mobile robots and field settings, such as search and rescue, transportation tasks, or automated vacuum cleaning.

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