Quantitative and Qualitative Assessments of Geometric Feature Accuracy Using a UAS-Lidar System for Building Surveying Applications

Abstract

Equipping unmanned aerial systems (UASs) with light detection and ranging (lidar) has been made possible with recent advancements, which has made these sensors compact and gradually more cost-effective. Despite the increased proliferation of UAS-lidar in several fields, the geometric accuracy of lidar-generated point clouds, together with their visual qualities, needs to be explored for building surveying applications. Considering that red−blue−green (RGB) cameras are the most prevalent UAS sensors in building surveying, a lidar- and an RGB camera-equipped UAS was deployed on a mixed infrastructure to simultaneously collect data and generate corresponding point clouds. Different geometric features from both RGB and lidar point clouds were measured and compared quantitatively against benchmark field observations. A qualitative analysis on the point clouds’ visual qualities was also performed and a sensor recommendation matrix was proposed based on desired application accuracy. Lidar has proven to be a viable alternative, providing better geometric accuracy, data quality, and clarity in all three dimensions.