基于七参数正形变换的数据驱动模型实现了机载LiDAR条带平差，算法借鉴了Robert(2004)的最小二乘表面匹 配思想，通过引入高斯–马尔科夫模型改进了原有算法，得到未知参数的最小无偏方差估计。实验采用两组实测数 据，分别考察了引入高斯–马尔科夫模型的必要性、算法效率以及迭代收敛性和算法精度。实验表明：(1)剖面检查吻 合且精度一致；(2)TerraMatch量测匹配精度，理想数据高程匹配误差小于0.05 m，数据质量不理想时误差稍大，但均 能成功匹配。

Based on Microsoft VS 2008 C++ platform, the least squares surface matching algorithm for airborne the LiDAR strip adjustment is realized. It refers to and improves Robert’s 3D surface matching algorithm by introducing the Gauss-Markoff model, acquiring the unbiased minimum variance estimation for the transformation parameters between adjacent strips. The real different data sets are used to validate the method. We study the need for using Gauss-Markoff model, effi ciency and iterative convergence of the algorithm, the matching accuracy. The experimental results demonstrate that after correction the point clouds show much better alignment and the vertical matching error is less than 0.05 m for idea data, while poor quality data the error is slightly larger.