In this paper, an adaptive unscented Kalman filter (AUKF) algorithm is applied to simultaneous localization and mapping (SLAM), based on adaptation of a scaling parameter. The scaling parameter is a design parameter in the unscented Kalman filter (UKF) which can improve the quality of the approximation. An adaptive method is designed to find the suitable value for the scaling parameter to improve the accuracy of estimation. It is demonstrated that the proposed methodology significantly reduces the state estimation error and improves the navigation accuracy of an autonomous vehicle. Also, it is highlighted that the computational cost is not much affected by increasing the number of observations, especially in the SLAM application in which the number of landmarks is growing through estimation. A comparison between UKF and AUKF algorithms is also provided for the SLAM application. The efficiency and the robustness of the proposed algorithm are investigated by applying noise of different orders in simulation results. In addition, non-credibility indices are used to compare the relative performance of AUKF and UKF. The results illustrate that AUKF-SLAM is more accurate than UKF-SLAM.

中文翻译：

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使用自适应无迹卡尔曼滤波器的 SLAM 效率

在本文中，基于缩放参数的自适应，将自适应无迹卡尔曼滤波器 (AUKF) 算法应用于同时定位和映射 (SLAM)。缩放参数是无迹卡尔曼滤波器 (UKF) 中的一个设计参数，可以提高近似的质量。设计了一种自适应方法来为缩放参数找到合适的值，以提高估计的准确性。结果表明，所提出的方法显着降低了状态估计误差并提高了自主车辆的导航精度。此外，需要强调的是，增加观察次数对计算成本的影响不大，特别是在 SLAM 应用中，其中地标数量通过估计而增加。还为 SLAM 应用程序提供了 UKF 和 AUKF 算法之间的比较。通过在仿真结果中应用不同阶数的噪声来研究所提出算法的效率和鲁棒性。此外，还使用非可信度指数来比较 AUKF 和 UKF 的相对表现。结果表明 AUKF-SLAM 比 UKF-SLAM 更准确。