| Approach of simultaneous localization and mapping based on local maps for robot |
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| 作者姓名： | 陈白帆 蔡自兴 胡德文 |
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| 作者单位： | [1]School of Information Science and Engineering, Central South University, Changsha 410083, China [2]College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China |
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| 基金项目： | 国家自然科学基金;国家重点基础研究发展计划(973计划) |
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| 摘 要： | An extended Kalman filter approach of simultaneous localization and mapping（SLAM） was proposed based on local maps A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.
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| 关 键 词： | 机器人 同期定位测图 扩展卡尔曼滤波器 局部画面 |
| 收稿时间： | 2006-02-28 |
| 修稿时间： | 2006-03-29 |
Approach of simultaneous localization and mapping based on local maps for robot |
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| Chen Bai-fan , Cai Zi-xing and Hu De-wen.Approach of simultaneous localization and mapping based on local maps for robot[J].Journal of Central South University of Technology,2006,13(6):713-716. |
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| Authors: | Chen Bai-fan Cai Zi-xing and Hu De-wen |
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| Affiliation: | (1) School of Information Science and Engineering, Central South University, Changsha, 410083, China;(2) College of Mechatronics and Automation, National University of Defense Technology, Changsha, 410073, China |
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| Abstract: | An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments. |
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| Keywords: | simultaneous localization and mapping extended Kalman filter local map |
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