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Titlebook: Robotic Mapping and Exploration; Cyrill Stachniss Book 2009 Springer-Verlag Berlin Heidelberg 2009 autonom.autonomous robot.knowledge.lear

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书目名称Robotic Mapping and Exploration
编辑Cyrill Stachniss
视频video
概述Recent research in the area of self-localization and mapping (SLAM) for autonomous robots.The presented solutions include uncertainty-driven exploration, active loop closing, coordination of multiple
丛书名称Springer Tracts in Advanced Robotics
图书封面Titlebook: Robotic Mapping and Exploration;  Cyrill Stachniss Book 2009 Springer-Verlag Berlin Heidelberg 2009 autonom.autonomous robot.knowledge.lear
描述."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..
出版日期Book 2009
关键词autonom; autonomous robot; knowledge; learning; mobile robot; robot; uncertainty
版次1
doihttps://doi.org/10.1007/978-3-642-01097-2
isbn_softcover978-3-642-10168-7
isbn_ebook978-3-642-01097-2Series ISSN 1610-7438 Series E-ISSN 1610-742X
issn_series 1610-7438
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Coordinated Multi-Robot Exploration to compensate for sensor uncertainty. As a result, the map can be expected to be more accurate. Multiple robots have also been shown to localize themselves more efficiently, especially when they have different sensor capabilities [42, 122].
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Actively Closing Loops During Explorationring models of unknown environments requires to simultaneously address three tasks, which are mapping, localization, and path planning. In the this chapter as well as in the two following ones, we focus on integrated approaches which aim to solve these three tasks simultaneously in order to build ac
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Recovering Particle Diversitylity of a map constructed by a mobile robot depends on its trajectory during data acquisition. This is due to the fact that the vehicle needs to relocalize itself during exploration in order to build an accurate model of the environment. Our loop-closing technique uses a heuristic stopping criterion
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