Tree Instance Segmentation and Traits Estimation for Forestry Environments Exploiting LiDAR Data Collected by Mobile Robots

Forests play a crucial role in our ecosystems, functioning as carbon sinks, climate stabilizers, biodiversity hubs, and sources of wood. By the very nature of their scale, monitoring and maintaining forests is a challenging task. Robotics in forestry can have the potential for substantial automation toward efficient and sustainable foresting practices. In this paper, we address the problem of automatically producing a forest inventory by exploiting LiDAR data collected by a mobile platform. To construct an inventory, we first extract tree instances from point clouds. Then, we process each instance to extract forestry inventory information. Our approach provides the per-tree geometric trait of "diameter at breast height" together with the individual tree locations in a plot. We validate our results against manual measurements collected by foresters during field trials. Our experiments show strong segmentation and tree trait estimation performance, underlining the potential for automating forestry services. Results furthermore show a superior performance compared to the popular baseline methods used in this domain.