Forest 3D Mapping and Tree Sizes Measurementfor Forest Management based on Sensing
Technology for Mobile Robots
Takashi Tsubouchi, Asuka Asano, Toshihiko Mochizuki, Shuhei Kondou, KeikoShiozawa, Mitsuhiro Matsumoto, Shuhei Tomimura, Shuichi Nakanishi, Akiko
Mochizuki, Yukihiro Chiba, Kouji Sasaki and Toru Hayami

Abstract This research work aims at application of sensing and mapping technology which has been developed with mobile robotics to equipment measurement offorest trees. One of the key information for forest management in artificial forest is
the records of the tree sizes and standing timber volume of a unit area.
The authors made measurement equipment as trial production which consistsof small sized laser range scanners with rotating (scanning) mechanism of them.
SLAM and related technologies are utilized for the information extraction. In development of SLAM algorithm for this application, sparseness of the standing trees
is considered. After the SLAM based mapping based on the data at several measurement points, we can obtain useful information such as a map of standing trees,
diameter at breast height of every tree, height at crown base (length of clear bole),and so on.
The authors will report the experiments in the forest and present the map andmeasured tree sizes.

T. Tsubouchi and A. Asano
University of Tsukuba, e-mail: tsubo@roboken.esys.tsukuba.ac.jp
T. Mochizuki, S. Kondou, K. Shiozawa and K. Sasaki
Adin Research Inc.
M. Matsumoto
Kurume National College of Technology
S. Tomimura, S. Nakanishi, A. Michizuki and T. Hayami
Forest Revitalization System Corp.
Y. Chiba
Forestry and Forest Products Research Institute

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T. Tsubouchi et al.

1 Introduction
A motivation of this research work arises from necessity of labor-saving in forest
mensuration for forest management. The present authors who long have research
work in mobile robotics try to apply sensing and mapping technologies into themensuration. Although the field work of the mensuration is laborious, manual measurement is still undergoing. Of the standard methods for forest survey, a belt transect method (Figure 1) could be employed [1]: e.g., 30m long and 10m wide with abase line running up and down slope in a survey plot. The distance between every
tree and the base line for determining tree location, its diameter at the breast height,
height at crown base (length of clear bole) and so on are measured within the belttransect. These measurements are performed in manual with measuring tapes by athree person party in standard. Measured parameters are once recorded in a fieldnote by one person, during the other two persons measure the survey items. It takes
approximately 30 minutes for a unit area at the earliest. After the measurement, they
input the data in an information processing facility.
These data will be processed to obtain standing timber volume and growth rateof forest stands. Such human-intensive manual measurements for forest survey arenecessary and common in forest management in the world [2]. Even though remotesensing approach such as airborne measurement with laser profiler has been being
utilized [3, 4, 5, 6, 7], it is necessary to establish models of correspondence between
every tree measurements in the forest and airborne measured data [8].
A problem for the manual measurement is time-consuming but insufficient tograsp the total environmental resource amount for targeted forests. For example, although Japanese government, as a country engaged in the Montreal Process whichaims at sustainable forest management, sets the forest monitoring points of 15 thou-Fig. 1 Manual measurement: (a) Measure diameter of each tree within a sampling unit area by atape measure. (b) Measure horizontal distance from the base line and its location on the base line.
Three persons cooperate in tree location measurement.

Forest 3D Mapping and Tree Sizes Measurement for Forest Management

3sands all over Japan, it is said that the number of the monitoring point is not enoughto obtain sufficient data for grasping the amount of the forest environmental resourcesuch as biomass or CO2 absorption. Increasing the number of the point is desired,
but it does not offer reality because of insufficiency of budgetary expense to employ
enough workers for the mensuration in short time.
The authors consider that replacing such manual measurements with equipment
measurements is a good idea to prompt the forest mensuration. Although airborne
approach ([3] and so on) is one method of equipment measurement, it provides
rather macroscopic view and will not provide microscopic observation. High cost tohire airplane does not solve the budgetary problem. There are several reports that alaser profiler is used in the forest [8]. However, the profiler itself is also expensive
and heavy so that it does not solve the budgetary problem and not have enoughmobility.
On the other hand, in robotics field, it becomes very popular to scan the environment by a small sized - light weighted laser scanner on a mobile robot. Sucha laser scanner is less expensive than the laser profiler. Furthermore, some appropriate SLAM technologies which are introduced in [9] can be applied to estimate
measured locations, to build a map and to assemble point clouds which are obtainedby the laser scanner at several measured locations. The authors consider that suchsensing and mapping technologies for mobile robots can be applied to the forest
mensuration. When we scan the forest by the small laser scanner and obtain point
clouds in many location, the assembled point clouds by SLAM will provide shapeand arrangement of trees, and will yield data of tree sizes and standing timber volume.
The authors illustrate the desired equipment as a rotating laser scanner at a topof a pole. A person can bring it in the forest. Once the person stands the pole on
the forest floor and does three dimensional scan there. The person will iterate suchscan at the several points to take three dimensional point clouds in sampling unit
area in the forest. Some of the ICP scan matching algorithms such as in [10, 11, 12]
are applicable to assemble all the obtained data to produce 3D map of the forest.
The authors, however, propose step by step matching algorism taking sparsenessof the standing trees into account before the application of ICP in this paper. Theproposed scan matching algorithm takes steepness of the slope in the forest into
account. Following the illustrated, the authors built up a scanner device and tried touse it in the forest. This paper reports a result of the 3D mapping from obtained dataof the rotating laser scanner at several points in the forest and results of diametermeasurement of trees in the map.

2 A Scanner and Measurement in the Forest
Figure 2 illustrates the scanner device. A small sized laser scanner UTM-X002S of
Hokuyo whose planer view angle is 270 degrees (0.6 degrees steps) is employed.
The scanner is placed on a stage driven by a motor and a crank (45 degrees) where

4T. Tsubouchi et al.

Fig. 2 A scanner device is illustrated in (a). An axle of the motor stands vertically followed bya special crank. An axis coincident with laser rotation axle of the scanner will rotate in precession by the rotation of the motor and crank mechanism. (b) The device is deployed for the trial
measurement.

Fig. 3 Measured environment and locations in the sampled slope. Measured locations are numbered from 1 to 5.

the authors made use of a mechanism proposed by [13]. This mechanism rotates
the center axis which is coincident with the laser rotation axle in precession so thatthe scanning plane is swayed. In the experiments, the period of stage swaying is
2 seconds. During the one period of the swaying, 200 scans of the laser scannerare performed. The one scan consists of 440 points of the range data. However,
400 points of the range data corresponding to 120 degrees from the center of thescanner are used. A coordinate system is defined on the scanner device such thatorigin is at the base of the device, x axis toward the front, z axis coincide with themotor axle and y axis forms the right hand system. The authors placed the scanner
device whose front or x axis of it looks up 15 degrees from the horizontal level

Forest 3D Mapping and Tree Sizes Measurement for Forest Management

5by adjusting the lengths of the legs of the tripod. Therefore, the x yplane of thescanner device also has an angle of 15 degrees from the ground.
Figure 3 illustrates the location to scan the environment and obtain point cloudsof surface of the trunks in the forest. In the experiment of this paper, the authors set
the baseline in the direction of the steepest slope at the sampled area as shown inFigure 3 (b). The steepness of the slope is 30 degrees in average there. The authors
set the device from the lowest to the highest location in approximately 5 metersinterval along the base line at the slope. The locations where the device is placed
are illustrated with ’+’ and numbered from 1 to 5 in Figure 3 (a). The front of thedevice is faced to the direction of the base line (Figure 2 (b)).

3 ICP Taking Account of Slope and Sparseness of Standing Trees
For the purpose of so called scan matching, ICP [11, 10, 12] is a popular method toassemble the point clouds of reference and current scans which are obtained at two
or more locations after scanning the surroundings by a laser scanner. The authors applied the ICP to the point clouds in planer forest and obtained enough performancefor the scan matching [14]. However, when it is applied in the forest with slope, thematching performance in vertical direction may become worse fundamentally because timbers are standing vertically and have less features on the trunk. Separationof point clouds into timbers and undergrowth is also necessary. Therefore, the scanmatching process is designed and proposed as follows:
1. extraction of timbers from the point clouds in a current scan and a reference scan,
2. making correspondence among the timbers in the current and the reference scan,
3. extraction of the slope ground level, and4. applying ICP for the scans.

3.1 Extraction of timbers
First a point cloud density histogram is obtained from a set of scan data of one location by projection onto the x y plane of the scanner device. However, they aretranslated onto logR θ plane, where Rx2  y2, θ atan2x y and x y isat the existing cloud point that z ignored. Figure 4 (a) illustrates an example histogram, where color presents the frequency of the cloud points – green presents low
frequency , blue mid and red high. When we see the histogram as a density functionof F log Rθ  θ , high frequency lasts at specific θ for the part of undergrowth.
On the other hand, it does not last at specific θ for the part of timber trunk surface.
Therefore, a filter is applied to reduce the density if high frequency lasts at specificθ . An example result of the filtering is illustrated in Figure 4 (b), then, the timbertrunk surfaces are extracted and identified.

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A least square circle fitting is applied to each identified surface of the trunk in
the x y coordinate and center location of the timber is obtained. The plot of such
points of the timber center on the x y plane is named “timber center map”. The
timber center map could be see such like constellations which are used for the next
step of making correspondence between the constellation of the reference and thecurrent scans.

3.2 Making correspondence among the timbers
In applying ICP, foreknowledge of the correspondence among the same objects in
the reference and current scan for beginning the ICP scan matching is helpful toavoid unexpected convergence because of local minima problem. For this purpose,correspondence among the timbers in the timber center map of the reference andcurrent scans must be established [15]. This process seems like to match constellations in the two star charts.
The algorithm to make correspondence among the timbers is summarized as follows (Figure 5) :1. Take three points of the timber center randomly in the timber center map of thereference scan and form a triangle.
2. Take three points of the timber center randomly in the timber center map of thecurrent scan and form a triangle.
3. Check whether the two triangles are congruent.
4. If the two are congruent, translate and rotate the current map so as to quadrate
the triangle in the reference scan with the one in the current scan. Calculate thesum of squared distance between every center point of a timber in the current
scan and a point in the reference scan within 100mm of the point of the current
scan. Count the number of ’paired’ points to calculate the sum.
5. Iterate 1. to 4. to find smallest sum and larger the number of the paired points in
4.

Fig. 4 Separation of point clouds into timber trunks and undergrowthForest 3D Mapping and Tree Sizes Measurement for Forest Management

76. When found in 4., translation and rotation parameters are obtained and the pairs
are recognized as corresponding timbers in the reference and the current scans.
7. Use the translation and rotation parameters obtained in 6. as the initial and findmost likelihood parameters for them by means of least square method to minimize the sum of distance between paired corresponding timbers in the timber
center maps of the reference and the current scans.
The sparseness of the distribution of the timber centers is considered in the process proposed here.

3.3 Extraction of the slope ground levelFor the purpose to obtain good convergence in the z direction, the slope groundlevel is extracted from the point clouds both in the reference and current scans. Thealgorithm is summarized as follows:
1. Define a grid in the scanner device x y plane.
2. Obtain a cloud point such that z coordinate value is the smallest within every
square grid lattice, which is a candidate of the ground and not the undergrowth
vegetation.
3. Associate all the points obtained in 2 and construct a surface by means of Delaunay triangulation.
Figure 6 (a) illustrates the extracted slope ground level. After the slope ground levelis determined, a part of point clouds of the surface of each corresponding timbertrunk in the different measured location is extracted at the same height (Figure 6(b) and (c)). These point clouds are subject to the ICP scan matching in the next
Section.

Fig. 5 Making correspondence among the timber centers in timber center map of the reference andthe current scans

8T. Tsubouchi et al.

3.4 Applying ICPMost steepest descent ICP is applied here. Translation and rotation parameters thatare obtained at the step 6 in Section 3.2 are used for initial location to begin the ICP.
All the point clouds extracted in Section 3.3 in the reference scan are used, but 60
randomly sampled cloud points extracted in Section 3.3 in the current scan are used
for the scan matching once. After convergence to obtain x and y translations androtations for the tree axes between the reference and the current scan, then obtainz translation to match the slope ground level. Iterate 1000 times for the random
samples of the point clouds of the current scan extracted in Section 3.3 and obtain
the best converged or minimal residue result. Figure 7 is an obtained 3D map wherepoint clouds measured at locations 1 to 4 in Figure 3 (a). In the right of Figure 7,
magnified image of a shrub is illustrated. Observing the branches of the shrub, scanmatching in all the translation and rotation components is performed well.

4 Extraction of Tree Data
From 3D map presented in Figure 7, the trunks of the timbers at the breast heightfrom the slope ground level are extracted (Figure 8). Figure 8 (a) presents the pointclouds of the trunks and fitted circles on them. This map must be useful for the forest maintenance to yield standing timber volume. Figure 8 (b) illustrates magnified
images of timbers of No. 1 and 8 to show how the circle fit on the point clouds. Thecircle and point clouds on the trunks of timber No. 1 seems to be desired fit. On theother hand for No.8, the point clouds are not necessarily coincides.

Fig. 6 Extraction of the slope ground level

Forest 3D Mapping and Tree Sizes Measurement for Forest Management

9Table 1 presents comparison of diameters at the breast height between the manual
measurement and the proposed equipment measurement. The diameter at the breast
height is also important parameter of a timber for forest maintenance. Six timbersof nine have error under 2 cm, other two have in the order of 3 cm and the other has
4.1 cm in error for the diameter. It is said that the desired performance for the error
is not more than 2 cm. In this point of view, the proposed equipment measurement
achieves in the level of the desired. However, more improvement to reduce the errors
is desired.

5 Conclusions
The authors presented necessity of equipment measurement for the forest mensuration and pointed it out that it is good application filed for sensing and SLAM technology for mobile robotics. A experimental setup for mechanically rotating small
sized laser scanner is prepared. The setup is operated in the forest and point clouds
at several locations are obtained. The authors proposed the step by step scan matching algorithm utilizing ICP taking the existence of the slope and sparseness of thetimbers into account. Such equipment measurement in the forest is proved to be ef-Fig. 7 Example result of ICP scan matching among the 4 locations illustrated in Figure 3 (a)10T. Tsubouchi et al.

Fig. 8 Example result of ICP scan matching among the 4 locations illustrated in Figure 3 (a)fective, however more improvement is necessary to obtain more accurate data. The
improvement is future work.

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