# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 1 Introduction  
Unmanned aerial vehicles (UAVs) were initially developed for military missions due to their large-scale mapping capabilities and ability to reach remote locations that people were not authorized to access [1]. In addition, these devices were also commonly used to develop DEM (Digital Elevation Models) [2] because they were more affordable than conventional methods [3], such as field surveying using a total scanning station [4], and they presented few take-off and landing requirements [5]. The new UAV models possess increased payload capacities, thus supporting technological enhancements such as sensors and cameras. Furthermore, UAVs have expanded their application possibilities, as they can now be used for infrastructure inspections, atmospheric research, fishing ground identification, environmental control, risk, natural disaster management, geological mining explorations, topography surveys, volumetric calculations [5], archaeological studies, plant monitoring [6] and crop biomass forecasting [7]. Among the available technologies that can be added to UAV systems are light detection and ranging (LiDAR) sensors. First introduced in the mid-1990s, these sensors produce a spatially accurate point cloud and are commonly used for mapping areas with high vegetation density, building segmentation and forest biomass inventories [8]. When implemented in a UAV, their data capture range is limited to the flight area, but they allow maximum coverage of the targeted area during each flight [9]. Even if the area is not very accessible, data collection times remain efficient against the times reported by conventional ground sensors, such as scanner tripods [10]. Therefore, trajectory reconstruction performance is essential for accurate LiDAR point cloud georeferencing based on UAV flights [11]. A previous study used a UAV to capture high-resolution images to generate surveying data throughout China's Altyn Tagh fault line. The resulting DEM evidenced a resolution of 0.065 m and an orthophoto of 0.016 m. Based on the data collected from the UAV, the authors measured recent seismic displacement at 7 \(\pm\) 1 m, supplemented with satellite images. In total, and due to multiple earthquakes, aggregated displacements of 15 \(\pm\) 2 m, 20 \(\pm\) 2 m and 30 \(\pm\) 2 m were measured [12]. LiDAR sensors have a wide variety of applications, such as using an adaptive thresholding algorithm to segment the LiDAR point cloud, which is then followed by a selection process [13]; collecting multi-temporal LiDAR data over 6 years to obtain annual growth rates and gains and losses in carbon storage in human-modified tropical forests [14] or assisting in the identification of vegetation that threatens the structural integrity of a given area under a Sustainable Conversation Plan [15]. Another study assessed the performance of a UAV equipped with a LiDAR by test target point detection comparisons, obtaining a mean square error of \(\pm\)10 cm without adjustment points and \(\pm\)4 cm using adjustment control points compared against a previous photographic 3D reconstruction [16]. In a comprehensive bias impact assessment for UAV-based LiDAR systems, an iterative calibration strategy was proposed to derive system parameters using different geometric characteristics, reducing the errors produced by mounting the LiDAR on the UAV and generating an accuracy of approximately 2 cm [17]. One of the more essential advantages authors find is the opportunity to customize the LiDAR system. The integration of a commercial UAV system with an Odroid minicomputer and the ROS system was used to perform flight missions with a low-weight platform that processes data obtained in real time without using a ground station [18]. A study for the 3D survey of forest ecosystems in China developed a low-cost LiDAR sensor system implemented in a UAV, where a multirotor UAV (eight rotors) and a Velodyne Puck VLP-16 were used as part of the hardware.