Feature Based Modeling and Mapping of Tree Trunks and Natural Terrain Using 3D Laser Scanner Measurement System

Abstract

This paper presents a novel approach to measure tree trunks and to model the ground using a 3D laser scanner. The 3D scanner, self-build using two 2D Sick scanners on a rotating base, measures each scan line approximately at 45° angle towards the ground and the trees. Single scan lines are segmented to find ground and tree returns. 3D point clouds from the surrounding forest are recorded while the measuring vehicle is moving. Sequential scan lines are joined together as the pose changes are reduced from the older buffered measurements. Laser odometry and inertial measurements are used to measure the pose changes. The ground is modeled by fitting a 1m grid to 3D point cloud extracted using a ground return detector. Tree trunks are searched from the 3D point cloud using a histogram approach to segment measurements into separate point clouds for each tree trunk. Tree trunks are modeled using ten circle features one on the other using the extracted point cloud. Instead of using the whole point cloud, mapping is done only for the extracted features and the travelled path to save computation time. Our method can detect nearly all tree trunks and measure them on short ranges of less than 8m with errors less than 4cm in diameter.

Application of Terrestrial Laser Scanning to Tree Trunk Bark Structure Characteristics Evaluation and Analysis of Their Effect on the Flow Resistance Coefficient

“…In particular, high resolution of the point clouds permits advanced analyses, including that of the tree trunk texture. The possibility to determine the bark structure characteristics supplements the already proven applications of TLS demonstrated by Kankare et al [34] and Hyyti and Visala [35], for determination of the diameter at breast height (DBH), tree heights, wood volume, tree stem ovality, structure and damage of bark, tree topology, and branch angles and diameters. Determination of the bark structure characteristics of trees permits a more accurate evaluation of the roughness coefficients of floodplains.…”

“…Methods based on TLS data have been used for automatic measurement of forest inventory attributes [27,33]. Numerous studies can be found in literature on calculation of the diameter at breast height (DBH), the height of the tree, the volume of wood, stem ovality, structure, and damage of the bark, tree topology, branch angles, and diameters [34,35]. Othmani et al [30,36], Brolly and Király [27], and Lin and Herold [37] have confirmed the possibility of identification of tree species using TLS data on the basis of 3D bark texture analysis.…”

Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

“…One strategy is to classify the lowest points in 3-D scanner snapshots as ground and subsequently segment the separated nonground point subsets in an urban environment [4]. In forest, Hyyti and Visala [14] differentiate points obtained from an all-terrain vehicle platform to ground and tree classes, labeling ground first with a line fitting method discussed in the following. Ground detection is, however, not always a prerequisite to all classification, as also shall be the case for noise and vegetation for PRC.…”

“…On the one hand, PRC is able to remove those points that have an abnormal range value in contrast to their neighborhood, i.e., noise and outliers that follow from reflections or background illumination [6]. On the other hand, PRC could be used to downsample, for example, ground points that are abundant in most mobile mapping scenarios, such as in urban environments [13] and in forest operations [9], [14]- [16]. This would save subsequent online computation, transmission, and postcomputation time, leading to wall-clock time savings on a larger industry scale.…”

Forestry crane posture estimation with a two‐dimensional laser scanner

“…However, terrestrial and mobile 3D LIDARS are becoming more widely used in the field of forest inventory (Hyyppä, Jaakkola, Chen, & Kukko, 2013). Furthermore, forest machines have been studied as mobile mapping platforms, for example, by Miettinen, Öhman, Visala, and Forsman (2007), Öhman et al (2008), and Hyyti and Visala (2013). Currently, commercial 3D LIDARS can provide accurate point cloud data (Schwarz, 2010), but they are quite expensive to be installed to machines operating in a forest for gathering data solely for forest inventory purposes.…”