A Multisensor UAV Payload and Processing Pipeline for Generating Multispectral Point Clouds

Abstract

Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. To date, however, few fully fledged drone systems exist that combine different sensing modalities in a way that complements the strengths and weaknesses of each. In this paper, we present our multimodal drone payload and sensor fusion pipeline, which allows multispectral point clouds to be generated at subcentimeter accuracy. To that end, we combine high-frequency navigation outputs from a professional-grade GNSS with photogrammetric bundle adjustment and a dedicated point cloud registration algorithm that takes full advantage of LiDAR's specifications. We demonstrate that the latter significantly improves the quality of the reconstructed point cloud in terms of fewer ghosting effects and less noise. Finally, we thoroughly discuss the impact of the quality of the GNSS/INS system on the structure from the motion and LiDAR SLAM reconstruction process.