Sufficient Condition for Estimation in Designin H∞ Filter-Based SLAM
Sufficient Condition for Estimation in Designin H∞ Filter-Based SLAM
复制标题
基于H∞滤波器的SLAM设计中估计的充分条件
DOI：
10.1155/2015/238131
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
and Toru Namerikawa
中科院分区：
文献类型：
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作者：
Nur Aqilah Othman;Hamzah Ahmad;and Toru Namerikawa
扩展卡尔曼滤波（EKF）常用于同时定位与地图构建（SLAM）中确定移动的机器人和地标的位置。然而，使用EKF也有一些缺点，即，对状态和噪声的高斯分布的要求，以及它需要尽可能小的初始状态协方差的事实。这促使研究人员寻找替代方法来减轻上述缺点。因此，本文提出了一种新的SLAM方法，即用H ∞滤波器代替EKF。在SLAM中实现H ∞滤波器时，需要适当地确定滤波器的参数，特别是γ，以避免有限逃逸时间问题。因此，本研究提出了一个充分条件的估计目的。考虑室内环境，分析了初始状态协方差的两种不同情况，以确保SLAM问题的最佳解存在沿着过程和测量噪声统计行为的考虑。如果不满足规定的条件，那么估计将表现出无限的不确定性，从而导致关于机器人和地标估计的错误推断。仿真结果表明，理论分析和我们以前的研究结果所建议的可靠性和一致性。
Extended Kalman filter (EKF) is often employed in determining the position of mobile robot and landmarks in simultaneous localization and mapping (SLAM). Nonetheless, there are some disadvantages of using EKF, namely, the requirement of Gaussian distribution for the state and noises, as well as the fact that it requires the smallest possible initial state covariance. This has led researchers to find alternative ways to mitigate the aforementioned shortcomings. Therefore, this study is conducted to propose an alternative technique by implementingH∞filter in SLAM instead of EKF. In implementingH∞filter in SLAM, the parameters of the filter especiallyγneed to be properly defined to prevent finite escape time problem. Hence, this study proposes a sufficient condition for the estimation purposes. Two distinct cases of initial state covariance are analysed considering an indoor environment to ensure the best solution for SLAM problem exists along with considerations of process and measurement noises statistical behaviour. If the prescribed conditions are not satisfied, then the estimation would exhibit unbounded uncertainties and consequently results in erroneous inference about the robot and landmarks estimation. The simulation results have shown the reliability and consistency as suggested by the theoretical analysis and our previous findings.