A SLAM Algorithm Based on Adaptive Cubature Kalman Filter
A SLAM Algorithm Based on Adaptive Cubature Kalman Filter
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一种基于自适应体积卡尔曼滤波器的SLAM算法
DOI：
10.1155/2014/171958
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发表时间：
2014-01-01
影响因子：
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通讯作者：
Fu, Yanwei
中科院分区：
文献类型：
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作者：
Yu, Fei;Sun, Qian;Fu, Yanwei
传统的同步定位与地图构建（SLAM）方法需要预测系统的数学模型和噪声统计的先验知识。然而，在许多实际应用中，噪声的先验统计是未知的或时变的，这将导致较大的估计误差，甚至导致发散。为了解决上述问题，本文在自适应容积卡尔曼滤波器（ACKF）的基础上，提出了一种基于容积卡尔曼滤波的SLAM（CKF-SLAM）算法。该算法通过引入Sage-Husa噪声统计估计器来估计未知系统噪声的统计参数。结合了
CKF-SLAM和自适应估计器相结合，新的ACKF-SLAM算法可以显著减小状态估计误差，有效提高SLAM系统的导航精度。在不同的情况下，这种新算法的性能进行了研究，通过数值模拟。结果表明，新的自适应CKF-SLAM算法可以有效地减小定位误差。与其他传统的SLAM方法相比，非线性SLAM系统的精度得到了显著提高。验证了所提出的ACKF-SLAM算法的有效性和可行性。
We need to predict mathematical model of the system and a priori knowledge of the noise statistics when traditional simultaneous localization and mapping (SLAM) solutions are used. However, in many practical applications, prior statistics of the noise are unknown or time-varying, which will lead to large estimation errors or even cause divergence. In order to solve the above problem, an innovative cubature Kalman filter-based SLAM (CKF-SLAM) algorithm based on an adaptive cubature Kalman filter (ACKF) was established in this paper. The novel algorithm estimates the statistical parameters of the unknown system noise by introducing the Sage-Husa noise statistic estimator. Combining the advantages of the
CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible.