Analysis of 3D Modeling Using LiDAR Velodyne for Tree Inventory Using the LIO-SAM Method

Abstract LiDAR technology has become increasingly popular for tree inventory, particularly when combined with techniques like LiDAR Inertial Odometry and Mapping, or LIO-SAM. LIO-SAM method of 3D modeling analysis for tree inventory using LiDAR is the main topic of this study. Planting trees is one way to lower the amount of carbon in the atmosphere, but doing so requires precise assessments of the trees’ architecture. LiDAR technology overcomes the spatial resolution constraints of satellite photography to enable very accurate three-dimensional modeling of real things. Since tiny LiDAR devices like TLS and Backpack are quite costly, this method was created as a workaround. The Ublox F9R GNSS receiver, Pixhawk orange IMU sensor, and Velodyne vlp 16 LiDAR sensor are the sensors that are used. Accurate point cloud creation is achieved via the LIO-SAM technique, which is subsequently converted into a 3D model of the trees. The precise measurement of tree dimensions required for numerous environmental and forest management applications is made possible by this research’s use of LiDAR. The outcomes demonstrate that the LIO-SAM approach offers excellent 3D modeling accuracy with a minimal average point cloud alignment error. The findings of georeferencing have an RMSE of less than one meter. Furthermore, a robust association has been shown between ground-truth data and 3D modeling outcomes obtained using this approach. The findings of this study can help with efforts to mitigate climate change and improve the management of forests.