Abstract
Diameter at breast height has been estimated from mobile laser scanning using a new set of methods. A 2D laser scanner was mounted facing forward, tilted nine degrees downwards, on a car. The trajectory was recorded using inertial navigation and visual SLAM (simultaneous localization and mapping). The laser scanner data, the trajectory and the orientation were used to calculate a 3D point cloud. Clusters representing trees were extracted line-wise to reduce the effects of uncertainty in the positioning system. The intensity of the laser echoes was used to filter out unreliable echoes only grazing a stem. The movement was used to obtain measurements from a larger part of the stem, and multiple lines from different views were used for the circle fit. Two trigonometric methods and two circle fit methods were tested. The best results with bias 2.3% (6 mm) and root mean squared error 14% (37 mm) were acquired with the circle fit on multiple 2D projected clusters. The method was evaluated compared to field data at five test areas with approximately 300 caliper-measured trees within a 10-m working range. The results show that this method is viable for stem measurements from a moving vehicle, for example a forest harvester.
Highlights
The developing field of precision forestry requires detailed information about forest stands, such as diameter at breast height (DBH) and stem positions of the trees within the stands [1]
We have presented a new system of methods for the estimation of DBH from a prototype of a mobile laser scanning system
Some insights have been made about the errors in laser scanning points on inclined surfaces, and a new clustering method using an intensity criterion has resulted in clusters of stem points that are more suitable for the circle fit
Summary
The developing field of precision forestry requires detailed information about forest stands, such as diameter at breast height (DBH) and stem positions of the trees within the stands [1]. The precision of satellite navigation is affected by multi-path effects induced by the forest canopy, which makes the positioning unreliable For these reasons, research regarding algorithms that can deliver accurate DBH from mobile laser scanners is of interest. One of the common problems is that the position of the scanner is hard to estimate with enough precision to co-register the point cloud successfully. In unstructured environments, such as forests, it is hard to predict the features in the environment, and such a positioning system is unreliable [19] Another problem is the precision of the commonly-used mobile laser scanners. We present methods to estimate the DBH of the trees using the special characteristics of this 2D/3D point cloud to line-wise extract features and only use the 3D information for positioning of the stems
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