平方根容积Rao-Blackwillised粒子滤波SLAM算法

宋宇; 李庆玲; 康轶非; 闫德立

doi:10.3724/SP.J.1004.2014.00357

平方根容积Rao-Blackwillised粒子滤波SLAM算法

doi: 10.3724/SP.J.1004.2014.00357

1.
北京交通大学电子信息工程学院北京 100044;
2.
哈尔滨工业大学机器人技术与系统国家重点实验室哈尔滨 150080;
3.
中国矿业大学(北京)机电与信息工程学院北京 100083

基金项目:

国家自然科学基金（60905055，61005070）;哈尔滨工业大学机器人技术与系统国家重点实验室开放基金（SKLRS-2009-ZD-04）;中央高校基本科研业务费（2014JBM014）资助

详细信息

作者简介:
李庆玲中国矿业大学(北京)机电与信息工程学院讲师.2009年在哈尔滨工业大学机器人技术与系统国家重点实验室获博士学位.主要研究方向为康复机器人系统. E-mail：qingling.li@ia.ac.cn

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出版历程
- 收稿日期: 2012-09-05
- 修回日期: 2013-02-04
- 刊出日期: 2014-02-20

SLAM with Square-root Cubature Rao-Blackwillised Particle Filter

1.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044;
2.
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150080;
3.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083

Funds:

Supported by National Natural Science Foundation of China (60905055, 61005070), Open Function of State Key Laboratory of Robotics and System of Harbin Institute of Technology (SKLRS-2009-ZD-04), and Fundamental Research Funds for the Central Universities of China (2014JBM014)

摘要

摘要: 面向大尺度环境中的移动机器人同时定位与地图构建（Simultaneous localization and mapping，SLAM）问题，提出平方根容积Rao-Blackwillised粒子滤波SLAM算法. 算法主要特点在于：1）采用容积律计算SLAM中的非线性函数高斯权重积分，达到减小SLAM非线性模型线性化误差、提高SLAM精度的目的；2）在SLAM中直接传播误差协方差矩阵的平方根因子，避免了耗费时间的协方差矩阵分解与重构过程，提高了SLAM计算效率. 通过仿真、实验将提出的SLAM算法与FastSLAM2.0、UFastSLAM两种算法进行对比，结果表明本文算法在SLAM性能上优于另两者.
- 移动机器人 /
- 同时定位与地图构建 /
- 粒子滤波 /
- 容积律 /
- 高斯权重积分
Abstract: In this paper, we derive a new large-scale environment simultaneous localization and mapping (SLAM) algorithm based on square-root cubature Rao-Blackwillised particle filter. The main contributions are: 1) to enhance the SLAM performance, the effective cubature rule is utilized to calculate the Gaussian weighted integral of the nonlinear function; 2) the covariance square-root factors are directly propagated in our SLAM process. Hence, the time-expensive decompositions on covariance matrixes are avoided. The performance of the proposed algorithm is compared with FastSLAM2.0 and UFastSLAM using a serial simulations and experiments. Results show that the proposed SLAM outperforms FastSLAM2.0 and UFastSLAM.
- Mobile robot /
- simultaneous localization and mapping(SLAM) /
- particle filter /
- cubature rule /
- Gaussian weighted integral(GWI)

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参考文献(14)

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