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针对点云拼接中双站数据的拼接必然会产生拼接误差的传递,最终导致整体拼接效果不佳的问题,该文讨论了基于增广扩展卡尔曼滤波的全局拼接,通过构造系统增广模型和系统观测模型,构建转换矩阵来对数据进行全局拼接:应用增广的卡尔曼滤波原理对各站点的位置姿态进行递归地估计,以同名点偏差为依据进行估计和校正,更新位置姿态,实现数据的全局拼接。最后采用上海地铁隧道三维点云数据进行基于增广扩展卡尔曼滤波的全局拼接实验。结果表明,此方法能够提高拼接精度,降低拼接误差。
In order to solve the problem of splicing errors in splicing of double-station data in point-cloud splicing, the splicing errors will inevitably result in the poor overall splicing effect. This paper discusses the global splicing based on augmented and extended Kalman filter. By constructing the system augmented model and System observation model to build a transformation matrix to global data splicing: the augmented Kalman filter principle to recursively estimate the position and attitude of each site, to estimate and correct based on the same name point deviation, to update the position and attitude, to achieve data Global splicing. Finally, the 3D stitching experiment based on augmented extended Kalman filter is carried out using the 3D point cloud data of Shanghai Metro Tunnel. The results show that this method can improve the stitching accuracy and reduce the stitching error.