When it comes to geospatial data, ensuring the accuracy and precision of LiDAR point clouds is paramount for creating reliable map products. In the February 2024 issue of Photogrammetric Engineering & Remote Sensing (PE&RS), a team from GeoCue, including Martin Flood, Dr. Nicolas Seube, and Darrick Wagg, authored an article that concentrated on LiDAR Point Cloud Quality Control: Automating Accuracy and Precision Testing.
With a focus on automating accuracy and precision testing, this article explains the evolving landscape of quality assurance in the realm of LiDAR data processing. As LiDAR technology continues to advance and find applications in diverse sectors, from large-scale surveying to drone-based data collection, the need for efficient quality control mechanisms becomes increasingly apparent. The article is now available in the February edition of the PE&RS Journal, or below.
Lidar Point Cloud Quality Control: Automating Accuracy and Precision Testing
The creation of map products from lidar point clouds requires rigorous quality control procedures. Review processes include manual inspection (“eyes on”) by a qualified technician in an interactive point cloud editing environment and increasingly automated quality checking tools to measure accuracy, precision, and other quality metrics. Increasing the efficiency of this review process is an important research area for lidar data producers and data users. Smaller lidar surveys, such as those collected by drones, require the same quality review and assessment tools for measuring accuracy and precision as larger scale surveys, so they can benefit from more automation as well.
In this article, we report on improved methods to automatically assess the accuracy and precision of lidar point clouds. We reference the ASPRS Positional Accuracy Standards for Digital Geospatial Data (2nd Edition) (the ‘ASPRS Standard’ or the ‘Standard’) throughout as the authoritative reference for lidar data quality assessment and reporting for map products. First, we will discuss the automatic detection of 3D lidar targets (“Accuracy Stars”) in point cloud data to measure vertical and horizontal accuracy and derive translation/rotation corrections for the data. In the second part of the article, we discuss our use of computational geometry to measure and report precision over large project areas using Principal Component Analysis (PCA). Combined, these two techniques allow for more automated quality checking of lidar point cloud accuracy and precision, reducing the need for manual interaction and scaling efficiently over large (or small) project areas.
Accuracy
Lidar accuracy assessment is typically done via classical methods inherited from photogrammetry. Vertical accuracy checking against the lidar surface at a known checkpoint (survey nail) is the most common approach in use today. Surface modelling of the lidar data is done using accepted Triangular Irregular Network (TIN) or Inverse Distance Weighted (IDW) methods.
For the complete paper CLICK HERE.
Note – If you liked this post click here to stay informed of all of the 3D laser scanning, geomatics, UAS, autonomous vehicle, Lidar News and more. If you have an informative 3D video that you would like us to promote, please forward to editor@lidarnews.com and if you would like to join the Younger Geospatial Professional movement click here