Generally, LiDAR sensors use near-infrared light, so in highly water contented areas such as marshes, the laser reflection intensity is weakened due to the absorption of a lot of light. In addition, during the IMU calibration process of the sensor, it is not possible to obtain a high-precision point cloud due to the shaking of the aircraft, accumulation of errors, and other factors. To post-process the acquired point cloud, it is necessary to separate the data that contains the vibrations of the aircraft, such as acceleration, rotation, and calibration, from the point cloud by aligning the trajectory of the UAV with the point cloud. However, manually separating the trajectory for a wide area of UAV flight can take a lot of time and can affect the consistency of the data. In this study, aim to extract a stable LiDAR point cloud by separating the trajectory of the UAV based on the following criteria for UAVs with a certain pattern of trajectory. First, separate the two trajectories by distinguishing between acceleration and cruising of the UAV. Second, separate two regions where the direction of the UAV's travel changes sharply. Finally, apply a process to separate the IMU calibration process. Through this process, can automatically extract the LiDAR trajectory data and select only the point cloud obtained at the same flight speed and altitude, thereby obtaining a point cloud density of a constant value. This study reduces the time required for separating and post-processing the trajectory of LiDAR data and enables the production of high-resolution terrain data for a wide area that needs to be flown at low altitudes.
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