Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping
Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping
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DOI：
10.3901/cjme.2011.04.693
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发表时间：
2011-07
影响因子：
4.2
通讯作者：
Song Yu;Yongduan Song;Qingling Li
中科院分区：
文献类型：
--
作者：
Song Yu;Yongduan Song;Qingling Li
同时定位与地图构建（SLAM）是移动的机器人在未知环境下工作的关键技术。FastSLAM算法是解决SLAM问题的一种常用方法，但存在两个主要缺点：一是粒子滤波器建议分布设计中观测信息不足导致粒子集退化;二是机器人非线性运动模型和环境观测非线性模型线性化导致误差积累。针对上述问题，提出了一种新的迭代西格玛点FastSLAM（ISP-FastSLAM）算法。该算法的主要贡献在于利用迭代sigma点卡尔曼滤波器（ISPKF），通过高斯-牛顿迭代最小化统计线性化误差，设计粒子滤波器的最优建议分布，并估计环境地标。在Rao-Blackwellized粒子滤波算法的基础上，提出了一种基于迭代sigma点粒子滤波（ISPPF）的机器人定位算法，该算法利用ISPKF精确地估计出机器人的位置分布;在第二部分中，利用一组ISPKF估计出环境地标。对ISP-FastSLAM算法与FastSLAM 2. 0算法和Unscented FastSLAM算法进行了仿真测试，比较了3种算法的性能。仿真和比较结果表明，ISP-FastSLAM算法在准确性和鲁棒性方面均优于其他两种算法。该算法为FastSLAM算法的优化研究提供了参考。
Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM (ISP-FastSLAM) algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter (ISPKF), which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts: in the first part, an iterated sigma point particle filter (ISPPF) to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.