Comparative Assessment of LiDAR Point Clouds Captured Using Inertial Labs RESEPI Gen-I-M2X and DJI Zenmuse L2 Sensors on UAS Platforms for Varying Terrain Conditions

Abstract

Unmanned Aerial Systems (UAS) equipped with LiDAR sensors are increasingly used for topographic surveying and mapping due to their ability to generate high-quality point clouds and penetrate vegetation. While photogrammetry remains a common approach in UAS mapping, its limitations in vegetated environments-where it often struggles to accurately capture ground surface details-have led to the adoption of LiDAR. LiDAR, with its capability to penetrate vegetation, provides more precise terrain observations and detailed representations of the underlying ground surface, even in densely vegetated areas. This study compares the performance of two LiDAR systems: the RESEPI Gen-I-M2X sensor from Inertial Labs, mounted on a WingtraOne Gen II fixed-wing UAS using post-processed kinematic (PPK) corrections; and the Zenmuse L2 sensor integrated with the DJI Matrice 350 RTK, utilizing real-time kinematic (RTK) corrections with additional PPK adjustments based on observation files from a local Continuously Operating Reference Station (CORS) acting as the base. Both platforms were flown over the same area, with point clouds analyzed across three distinct conditions: open terrain, urban development, and wooded areas. A total of 135 GNSS-measured reference points were deployed, with 10 designated as ground control points (GCPs) to enhance vertical accuracy, while the remaining 125 points served as checkpoints for validation. Some checkpoints were located at the center of manhole covers, others at painted arrow markers on roadways, but the majority-especially those in wooded areas-were natural points without signalization. The Zenmuse L2 datasets were processed in DJI Terra, generating point clouds with and without GCP integration. In contrast, the RESEPI Gen-I-M2X datasets relied solely on PPK corrections, as the processing software does not support GCP integration. This study evaluates the accuracy and noise levels of the point clouds in varying environments, focusing on terrain representation by the LiDAR sensors. The findings provide insights into the strengths and limitations of each platform and correction strategy, offering guidance for selecting appropriate UAS LiDAR systems for specific surveying and mapping applications. This research contributes to the growing body of work on UAS LiDAR by highlighting key factors that influence data quality, including sensor selection, correction methods, and environmental conditions.