Automatic three‐dimensional mapping for tree diameter measurements in inventory operations

Abstract

Forestry is a major industry in many parts of the world, yet this potential domain of application area has been overlooked by the robotics community. For instance, forest inventory, a cornerstone of efficient and sustainable forestry, is still traditionally performed manually by qualified professionals. The lack of automation in this particular task, consisting chiefly of measuring tree attributes, limits its speed, and, therefore, the area that can be economically covered. To this effect, we propose to use recent advancements in three-dimensional mapping approaches in forests to automatically measure tree diameters from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging and large-scale forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new publicly-available dataset which includes four different forest sites, 11 trajectories, totaling 1458 tree observations, and 14,000 m 2. From our extensive validation, we concluded that our mapping method is usable in the context of automated forest inventory, with our best diameter estimation method yielding a root mean square error of 3.45 cm for our whole dataset and 2.04 cm in ideal conditions consisting of mature forest with well-spaced trees. Furthermore, we release this dataset to the public (https://norlab.ulaval.ca/research/montmorencydataset), to spur further research in robotic forest inventories. Finally, stemming from this large-scale experiment, we provide recommendations for future deployments of mobile robots in a forestry context.

Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

“…Our tree detection and size errors results compared well with recent studies making use of terrestrially-based lidar systems for forest inventories, including estimates of individual tree height. Comparable studies examining individual tree diameters extracted from MLS and TLS datasets often yield between 1 and 5 cm RMSE, and commonly between 3 and 4 cm [15,21,43,47]. Estimates of tree height from terrestrially-based lidar datasets vary considerably, but generally tend to underpredict [14,15,21].…”

Advances in Forest Robotics: A State-of-the-Art Survey

“…They used a 3D graph-SLAM approach, Random Sample Consensus (RANSAC), to identify the soil and Signed Distance (SD) to represent the standard height of measurements for the estimation of DBH. Due to the chosen environment (Figure 6a) having few obstacles, the proposed method obtained a mean estimation error of DHB of 2 cm, and, for tree positioning accuracy, the mean error was 0.0476 m. In a similar work, Tremblay et al [32] analyzed several methods of automatic threedimensional mapping for tree diameter measurements in inventory operations. After considering a cylinder fitting for DBH estimation, different combinations of methods for determining circles were tested.…”

“…After performing several tests, the DBH estimation method that obtained the best performance was the vertical Axis + nonlinear least-squares cylinder, while Axis linear least-squares obtained the worst. The environment that obtained the best performance was mature, with well-spaced trees and visible trunks [32].…”

“…Figure 5. Examples of robotic applications used in forests for inventory operations: (a) FGI ROAMER R2 [27], (b) Komatsu Forest 931.1 [9], (c) AgRob V18 [28], (d) Forest robot 1[29], (e) Forest robot 2[30], (f) Superdroid[31] and (g) Husky A200[32].…”

“…Figure 6. Tree detection during navigation activities: (a) results obtained by[31] and (b) results obtained by[32].…”