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微惯性/激光雷达(MEMS IMU/LADAR)组合导航系统在室内应用时,由于室内结构化环境下环境特征(如点和线段)分布稀疏,传统的单一特征匹配算法存在观测盲区,易造成导航定位参数估计误差大的问题。基于此,研究了激光雷达自适应数据分割方法的点和线段的特征提取算法,提出了基于混合特征匹配观测模型的MEMS IMU/LADR扩展卡尔曼滤波(EKF)算法。同时,设计了MEMS IMU/LADR组合导航试验样机,在室内环境下通过试验对滤波算法进行了验证。结果表明:提出的算法在室内结构化环境下相比传统单一点或线特征匹配组合定位算法的定位精度可提高60%,对于小型旋翼无人飞行器在室内结构化环境中的高精度定位具有较高的参考意义。
Because of the sparse distribution of environmental features (such as points and line segments) in the indoor structured environment, the traditional single-feature matching algorithm has a blind spot for observation in the indoor application due to the MEMS IMU / LADAR integrated navigation system. Parameter estimation error problem. Based on this, the feature extraction algorithm of points and line segments of lidar adaptive data segmentation method is studied, and the MEMS IMU / LADR extended Kalman filter (EKF) algorithm based on hybrid feature matching observation model is proposed. At the same time, the MEMS IMU / LADR integrated navigation test prototype is designed, and the filtering algorithm is verified through experiments in indoor environment. The results show that the proposed algorithm can improve the positioning accuracy by 60% compared with the traditional single point or line feature matching combined positioning algorithm in indoor structured environment, which is more accurate than the small rotor UAV in indoor structured environment High reference value.