Light Detection and Ranging (LiDAR) systems are known to capture high density and accuracy data much more efficiently than other surveying methods. Therefore they are used for many applications, e.g. mobile mapping and surveying, 3D modelling, hazard detection, etc. However, while the accuracy of the laser measurements is very high, the accuracy of the resulting 3D point cloud is greatly affected by the geo-referencing accuracy. This is especially problematic for mobile laser scanning systems, where the LiDAR is installed on a moving platform, e.g. a vehicle, and the point cloud is geo-referenced by the data provided by a navigation system. Owing to the complexity of the surrounding environments and external conditions, the accuracy of the navigation system varies and thereby changes the quality of the point cloud. Conventional methods for correcting the point cloud accuracy either rely heavily on manual work or semi-automatic registration methods. While they can provide geo-referencing under different conditions, each has their own problems. This paper presents a semi-automated geo-referencing trajectory correction method by extracting features from the pre-processed point cloud and integrating this information to reprocess the navigation trajectory which is then able to produce better quality point clouds. The method deals with the changing errors within a point cloud dataset, and reducing the trajectory error from metre level to decimetre level, improving the accuracy by at least 56%. The accuracy of the regenerated point cloud then becomes suitable for many accuracy-demanding monitoring and change detection applications.

众所周知，光检测和测距（LiDAR）系统比其他测量方法更有效地捕获高密度和精度数据。因此，它们可用于许多应用，例如移动制图和勘测，3D建模，危险检测等。但是，尽管激光测量的精度非常高，但是生成的3D点云的精度却受到地理信息的极大影响。参考精度。对于将激光雷达安装在移动平台（例如车辆）上并且点云由导航系统提供的数据进行地理参考的移动激光扫描系统而言，这尤其成问题。由于周围环境和外部条件的复杂性，导航系统的精度会发生变化，从而改变点云的质量。校正点云精度的常规方法在很大程度上依赖于人工工作或半自动配准方法。尽管它们可以在不同条件下提供地理参考，但是每个都有其自身的问题。本文提出了一种半自动的地理参考轨迹校正方法，该方法通过从预处理的点云中提取特征并将这些信息集成以重新处理导航轨迹，从而能够生成质量更好的点云。该方法处理点云数据集中的变化误差，并将轨迹误差从仪表级别降低到分米级别，从而将准确性提高至少56％。再生的点云的精度随后变得适合于许多对精度要求很高的监视和变化检测应用程序。