| 書目名稱 | FastSLAM |
| 副標題 | A Scalable Method fo |
| 編輯 | Michael Montemerlo,Sebastian Thrun |
| 視頻video | http://file.papertrans.cn/342/341340/341340.mp4 |
| 概述 | From the winners of the DARPA Grand Challenge.First book on the market about FastSLAM, which is the most influential recent contributions to the SLAM (Simultaneous Localization and Mapping) problem fo |
| 叢書名稱 | Springer Tracts in Advanced Robotics |
| 圖書封面 |  |
| 描述 | .This monograph describes a new family of algorithms for the simultaneous localization and mapping problem in robotics (SLAM). SLAM addresses the problem of acquiring an environment map with a roving robot, while simultaneously localizing the robot relative to this map. This problem has received enormous attention in the robotics community in the past few years, reaching a peak of popularity on the occasion of the DARPA Grand Challenge in October 2005, which was won by the team headed by the authors. The FastSLAM family of algorithms applies particle filters to the SLAM Problem, which provides new insights into the data association problem that is paramount in SLAM. The FastSLAM-type algorithms have enabled robots to acquire maps of unprecedented size and accuracy, in a number of robot application domains and have been successfully applied in different dynamic environments, including the solution to the problem of people tracking. . |
| 出版日期 | Book 2007 |
| 關(guān)鍵詞 | Augmented Reality; Markov; Peak; Tracking; actor; algorithm; algorithms; filtering; robot; robotics |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-46402-0 |
| isbn_softcover | 978-3-642-07978-8 |
| isbn_ebook | 978-3-540-46402-0Series ISSN 1610-7438 Series E-ISSN 1610-742X |
| issn_series | 1610-7438 |
| copyright | Springer-Verlag Berlin Heidelberg 2007 |
|Archiver|手機版|小黑屋|
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