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为解决对空间未知目标的相对位置、姿态估计问题,以激光成像雷达作为测量敏感器,提出了基于扩展Kalman滤波(EKF,Extended Kalman Filter)的相对位姿估计算法。采用迭代最近点算法(Iterative Closest Point,ICP)对激光雷达的点云测量数据进行解算,得到相对位姿粗值并将其作为位姿估计算法的测量输入。以相对姿态、角速度、惯量比、相对位置、相对速度和目标测量参考系的位姿作为滤波状态,算法在对相对位置和姿态估计的同时,可辨识出目标的未知参数。为提高数值仿真的可信度,用Geomagic软件模拟点云测量。采用Matlab进行数值仿真,验证了新算法的有效性。
In order to solve the problem of the relative position and attitude estimation of unknown targets in space, the relative pose estimation algorithm based on Extended Kalman Filter (EKF) is proposed using laser imaging radar as the measurement sensor. The Iterative Closest Point (ICP) is used to solve the lidar data. The relative position and roughness values are obtained and used as the measurement input of pose estimation algorithm. With relative attitude, angular velocity, inertia ratio, relative position, relative velocity and pose of target reference system as filter states, the algorithm can recognize the unknown parameters of the target while estimating relative position and attitude. To improve the reliability of numerical simulation, Geomagic software is used to simulate point cloud measurement. Numerical simulation with Matlab verifies the effectiveness of the new algorithm.