林床の地形計測を想定した低価格モバイルLiDARスキャナの運用に関する検証

抄録

LiDAR technology involves the use of lasers to measure the distances to objects with varying ranges and resolutions based on the environment. LiDAR-SLAM enables simultaneous self-positioning and mapping, so high-density point clouds can be acquired without relying on the GNSS. Low-cost MLSs are being increasingly adopted in engineering and automated driving owing to their affordability. However, studies on natural environment measurements are limited. As such, the aim in this study aimed was to summarize the methods of and challenges with implementing low-cost MLS for forest floor topographic surveying and to verify the optimal operating conditions. The results showed that with low- cost MLS using LiDAR-SLAM, the density of point cloud decreased with increasing irradiation distance, and became below the measurement limit in the case of more than 200 m, because of the measurement principle of SLAM and the influences of and the environment. The point cloud density varied depending on the object, particularly under wet conditions. The key finding was that the characteristics of low-cost MLSs and the SLAM drift phenomenon must be taken into account because the optimal measurement modes depend on vegetation cover and surface humidity, requiring ingenuity for measurements of obstructed environments such as the forest floor.