With the objective of improving the registration of lidar point clouds produced by kinematic scanning systems, we propose a novel trajectory adjustment procedure that leverages on the automated extraction of selected reliable 3D point–to–point correspondences between overlapping point clouds and their joint integration (adjustment) together with raw inertial and GNSS observations. This is performed in a tightly coupled fashion using a dynamic network approach that results in an optimally compensated trajectory through modeling of errors at the sensor, rather than the trajectory, level. The 3D correspondences are formulated as static conditions within the dynamic network and the registered point cloud is generated with significantly higher accuracy based on the corrected trajectory and possibly other parameters determined within the adjustment. We first describe the method for selecting correspondences and how they are inserted into the dynamic network via new observation model while providing an open-source implementation of the solver employed in this work. We then describe the experiments conducted to evaluate the performance of the proposed framework in practical airborne laser scanning scenarios with low-cost MEMS inertial sensors. In the conducted experiments, the method proposed to establish 3D correspondences is effective in determining point–to–point matches across a wide range of geometries such as trees, buildings and cars. Our results demonstrate that the method improves the point cloud registration accuracy ($\sim$5$\times$ in nominal and $\sim$10$\times$ in emulated GNSS outage conditions within the studied cases), which is otherwise strongly affected by errors in the determined platform attitude or position, and possibly determine unknown boresight angles. The proposed methods remain effective even if only a fraction ($\sim$0.1%) of the total number of established 3D correspondences are considered in the adjustment.

为了改进运动扫描系统产生的激光雷达点云的配准，我们提出了一种新的轨迹调整程序，该程序利用重叠点云之间的选定可靠 3D 点对点对应关系的自动提取及其联合集成（调整) 连同原始惯性和 GNSS 观测。这是使用动态网络方法以紧密耦合的方式执行的，该方法通过对传感器而不是轨迹水平的误差建模来产生最佳补偿轨迹。3D 对应关系被表述为动态网络中的静态条件，并且基于校正的轨迹和可能在调整中确定的其他参数，以显着更高的精度生成配准点云。我们首先描述了选择对应关系的方法以及如何通过新的观察模型将它们插入动态网络，同时提供本工作中使用的求解器的开源实现。然后，我们描述了为评估所提出的框架在具有低成本 MEMS 惯性传感器的实际机载激光扫描场景中的性能而进行的实验。在进行的实验中，所提出的建立 3D 对应关系的方法在确定树木、建筑物和汽车等各种几何形状的点对点匹配方面是有效的。我们的结果表明，该方法提高了点云配准精度（然后，我们描述了为评估所提出的框架在具有低成本 MEMS 惯性传感器的实际机载激光扫描场景中的性能而进行的实验。在进行的实验中，所提出的建立 3D 对应关系的方法在确定树木、建筑物和汽车等各种几何形状的点对点匹配方面是有效的。我们的结果表明，该方法提高了点云配准精度（然后，我们描述了为评估所提出的框架在具有低成本 MEMS 惯性传感器的实际机载激光扫描场景中的性能而进行的实验。在进行的实验中，所提出的建立 3D 对应关系的方法在确定树木、建筑物和汽车等各种几何形状的点对点匹配方面是有效的。我们的结果表明，该方法提高了点云配准精度（$～$5$\times$在名义上和$～$10$\times$在所研究案例中的模拟 GNSS 中断条件下），否则会受到所确定平台姿态或位置误差的强烈影响，并可能确定未知的视轴角。即使只有一小部分（$～$在调整中考虑了已建立的 3D 对应总数的 0.1%)。