Wang Yang, Xie Chenxi, Liu Yinfeng, Zhu Jialin, Qin Jixing
Department of Automation, Beijing Information Science and Technology University, Beijing 102206, China.
Department of Applied Science, Beijing Information Science and Technology University, Beijing 102206, China.
Sensors (Basel). 2024 Sep 29;24(19):6299. doi: 10.3390/s24196299.
In recent years, the simplified computation of position and velocity changes in nonlinear systems using Lie groups and Lie algebra has been widely used in the study of robot localization systems. The unscented Kalman filter (UKF) can effectively deal with nonlinear systems through the unscented transformation, and in order to more accurately describe the robot localization system, the UKF method based on Lie groups has been studied successively. The computational complexity of the UKF on Lie groups is high, and in order to simplify its computation, the Lie groups are applied to the manifold, which efficiently handles the state and uncertainty and ensures that the system maintains the geometric constraints and computational simplicity during the updating process. In this paper, a multi-sensor fusion localization method based on an unscented Kalman filter on manifolds (UKF-M) is investigated. Firstly, a system model and a multi-sensor model are established based on an Autonomous Underwater Vehicle (AUV), and a corresponding UKF-M is designed for the system. Secondly, the multi-sensor fusion method is designed, and the fusion method is applied to the UKF-M. Finally, the proposed method is validated using an underwater cave dataset. The experiments demonstrate that the proposed method is suitable for underwater environments and can significantly correct the cumulative error in the trajectory estimation to achieve accurate underwater localization.
近年来，利用李群和李代数对非线性系统中的位置和速度变化进行简化计算，在机器人定位系统研究中得到了广泛应用。无迹卡尔曼滤波器（UKF）能够通过无迹变换有效处理非线性系统，为了更准确地描述机器人定位系统，基于李群的UKF方法相继得到研究。UKF在李群上的计算复杂度较高，为简化其计算，将李群应用于流形，从而有效处理状态和不确定性，并确保系统在更新过程中保持几何约束和计算简便性。本文研究了一种基于流形上无迹卡尔曼滤波器（UKF-M）的多传感器融合定位方法。首先，基于自主水下航行器（AUV）建立系统模型和多传感器模型，并为该系统设计相应的UKF-M。其次，设计多传感器融合方法，并将该融合方法应用于UKF-M。最后，利用水下洞穴数据集对所提方法进行验证。实验表明，所提方法适用于水下环境，能够显著校正轨迹估计中的累积误差，实现精确的水下定位。
