Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with accurate exterior orientations instead of using ground control points. The MLS trajectory is estimated based on observation equations resulting from these corresponding points, the original IMU observations, and soft constraints on the pitch and yaw rotations of the vehicle. We analyse the quality of the trajectory enhancement under several conditions where the experiments were designed to test the influence of the number and quality of corresponding points and to test different settings for a B-spline representation of the vehicle trajectory. The method was tested on two independently acquired MLS datasets in Rotterdam by enhancing the trajectories and evaluating them using checkpoints. The RMSE values of the original GNSS/IMU based Kalman filter results at the checkpoints were 0.26 m, 0.30 m, and 0.47 m for the X-, Y- and Z-coordinates in the first dataset and 1.10 m, 1.51 m, and 1.81 m in the second dataset. The latter dataset was recorded with a lower quality IMU in an area with taller buildings. After trajectory adjustment these RMSE values were reduced to 0.09 m, 0.11 m, and 0.16 m for the first dataset and 0.12 m, 0.14 m, and 0.18 m for the second dataset. The results confirmed that, if sufficient tie points between the point cloud and aerial imagery are available, the method supports geo-referencing of MLS point clouds in urban canyons with a near-decimetre accuracy.

城市峡谷中建筑物的多径效应和信号障碍会导致GNSS测量不准确，从而导致移动激光扫描（MLS）系统的估计轨迹出现误差；因此，派生的点云会失真并失去空间一致性。我们利用MLS数据和航拍图像之间的对应点（具有精确的外部方向）而不是使用地面控制点，来获得分米级的轨迹精度。基于这些对应点，原始IMU观测值以及车辆俯仰和偏航旋转的软约束得出的观测方程，可以估算MLS轨迹。我们在几种条件下分析了轨迹增强的质量，在这些条件下设计了实验，以测试相应点的数量和质量的影响，并测试车辆轨迹的B样条曲线的不同设置。通过增强轨迹并使用检查点对其进行评估，在鹿特丹的两个独立获取的MLS数据集上对该方法进行了测试。在第一个数据集中，基于原始GNSS / IMU的Kalman滤波结果在检查点的RMSE值分别为X，Y和Z坐标为0.26 m，0.30 m和0.47 m，分别为1.10 m，1.51 m和1.81第二个数据集中的m。后一个数据集是在建筑物较高的区域中以较低质量的IMU记录的。进行轨迹调整后，对于第一个数据集，这些RMSE值分别减小为0.09 m，0.11 m和0.16 m，并减小为0。第二个数据集分别为12 m，0.14 m和0.18 m。结果证实，如果在点云和航拍图像之间有足够的联系点，则该方法可以以近十米的精度支持城市峡谷中MLS点云的地理参考。