Comparison of Different Circular Feature Extraction Algorithms for ...

An ASABE – CSBE/ASABE Joint Meeting Presentation Paper Number: 141898885

Comparison of Different Circular Feature Extraction Algorithms for Tree Trunk at Different Scanning Angles Wang, Yaxiong; Kang, Feng; Li, Wenbin The school of Technology, Beijing Forestry University, Beijing, China.

Written for presentation at the 2014 ASABE and CSBE/SCGAB Annual International Meeting Sponsored by ASABE Montreal, Quebec Canada July 13 – 16, 2014 Abstract. Feature extraction of tree trunk cross-section shape plays an important part in targeted barrier application, which is the precondition of precise targeted spray. During targeted spray operation, the accurate information of tree trunk cross-section should be obtained firstly and fed to spray system. According to trunk cross-section shape characteristics, it is reasonable to apply circular feature extraction. In this research, three circular feature extraction algorithms including Kasa’s Method, Modified Least-Squares Method (MLS Method) and Average of Intersections Method (AI Method) were evaluated and compared based on laser scanner data. A SICK LMS 511 laser range finder was installed on a tripod horizontally scanning tree trunks, whose scan frequency and angle resolution were set fixed. The accuracy of the three algorithms were observed and compared in order to figure out if the results had errors when scan the same direction of one trunk at different angles using the three algorithms. Keywords. Tree trunk cross-section, Circular feature extraction, Targeted spray, Laser range finder

Introduction With development of the information society, artificial identification of external things is being replaced gradually by automated intelligent identification. In recent years, the emergence of ultrasound technology, machine vision, spectroscopy technology, laser ranging technology and so on has reduced human workload, saved time and cost. In forestry, automatic identification technologies have also being progressing and improving steadily, and obtaining objects information using laser scanning data among these technologies is an important direction. Despite it only has limited information access compared to machine vision, other advantages such as accurate measurement, less affected by light, simple post-processing, make it practical. Conducting forest operations using information from laser scanning, like spraying, timber harvesting can ensure efficient, accurate and stable operations. Each frame of data collected by a laser scanner without the frame head and frame end is a set of point cloud data consisting of multiple data points, and every point is made up of angle and distance information. Processing of these data requires related algorithms, and then the information needed can be obtained by

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these algorithms. In this research, the experiment of data collection and information extraction for multiple trees with three algorithms were conducted, the three algorithms were Kasa’s Method, Modified Least-Squares Method (MLS Method) and Average of Intersections Method (AI Method). The information extracted was the trunk diameter and the distance between the laser scanner and the trunk center. The trunks distribute in different directions to the laser scanner in the field of view, whether it has errors or not when identify the same trunk at different angles is an important problem to fix. This paper discusses the preliminary verifying experiment which verified if the results have errors when scan the same direction of one trunk at different angles.

Materials and Methods System Hardware Figure 1 shows the hardware components of the detection system. In this experiment, the main detection device was a 2D laser scanner (LMS511 PRO, SICK Company, German) mounted on a tripod. The Client/Server(C/S) communication mode was used for communication. The laser scanner played as the server in experiment while the client was a laptop PC (Dell E5400, Microsoft Windows XP Professional, Intel(R) Core(TM)2 Duo CPU P8700 @ 2.53GHz, 3.45GB RAM). The laptop PC interfaced with the laser scanner via Ethernet. The whole system was powered by a 24V DC lithium battery. A high power laser pointer was used for calibrating the laser scanning position on the trunk. The pointer was fixed on a holder. An angle gauge was installed right beneath the pointer for measuring the angle of the scanned trunk in the field of view (fig. 1). Laser pointer

LMS 511

Lithium battery

Laser holder

Tripod

Figure 1. Hardware components of the laser scanning device.

System Software There were two software systems in this experiment. One was commercial available supplied by SICK Company in German named SOPASTM. It can be only used for preliminary processing of received data and displaying the point cloud data on the screen after coordinate transformation. It doesn’t have the function of fitting and clustering the point cloud data. The role of this software was to assist the experiment software which was the main software in the experiment. The experiment software was developed in VC++ using MFC framework. The interface of the experiment software is shown in figure 2.

2014 ASABE – CSBE/SCGAB Annual International Meeting Paper

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Figure 2. Interface of the experiment software.

Experiment procedure Preheat experiment of the laser scanner The purpose of this experiment was to figure out the performance of LMS511 (whether it needs preheating or not). The laser scanner faced a fixed object (i.e. a wall) and horizontally and continuously scanned the wall for 30 minutes to see whether the distance measured by the scanner was stable or not. The results demonstrated that the distance measured in 30 minutes was very stable (the error was 1~8mm at a distance of 2m). Based on this conclusion, in the following experiments the scanner only was given a short warm-up time (10 minutes). The calibration experiment of laser pointer’s elevation There were some errors when the laser pointer was fixed to the holder of the scanner. So when flat ground had been selected and relevant leveling had been accomplished, the parallel of the laser pointer’s ray and the scanning plane of the scanner still couldn’t be ensured. So the calibration of laser scanning beam and the angle of the crosshead and the horizontal plane needed to be done. For the measurement of the elevation, another tripod needed to be erected on flat ground, and a cardboard was clipped on the tripod. It is shown in figure 3. Selected two different distances: 60and 90mm, then opened the SOPAS, continuously adjust the position of the cardboard via tripod. When the laser scanned to the lower edge of the cardboard, it will be displayed on the SOPAS. At this time, the laser scanning plane coincided with the lower edge of cardboard was believed. Then the distance of the lower edge of cardboard and the crosshead on the cardboard which was come from laser pointer was measured. The experiment was repeated for three times to obtain the average. In this experiment, there were two quantities unknown to us, one was the value of elevation expressed with “α”, and the other was the distance of laser pointer and laser scanning plane expressed with “L” (fig.4). The two unknown quantities could be gained from one set of equations, and the equations are shown as follows:

L1 tan   L  S1

(1)

L2 tan   L  S 2

(2)

Where, L1 is the distance between the first two tripods, it was 60mm in this experiment; L2 is the distance between the second two tripods, it was 90mm; S1 is the distance between the lower edge of cardboard and the crosshead of laser pointer on the cardboard in first measurement; S2 is the distance of the lower edge of cardboard and the crosshead of laser pointer on the cardboard in second measurement.


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Figure 3. The experiment equipment of laser pointer’s elevation calibration.

Figure 4. The schematic diagram of laser pointer’s elevation calibration.

The experiment to verify the accuracy of data collection at different angles Experimental place was located behind the library of Beijing Forestry University, and the experimental subjects were eucommia ulmoides. During the experiment, time, weather condition, temperature and humidity were also recorded. Firstly, level experimental ground needed to be select to facilitate leveling. The laser was set at 1, 2, and 3 m from the tree, and a plumb was used to ensure specific position. This is shown in figure 5. The trunk selected should be similar to circular, or it might cause failure when using algorithms. The experiment also required clean background to avoid the impact of noise in the laser scanning range.


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Figure 5. The location equipment of experiment using a plumb.

The device needed to be leveled after the preparatory work. There were two steps to do this: the first was leveling tripod mainly rely on their own leveling device, such as the built-in bubble; the second was leveling the laser scanning plane with the horizontal ruler. So when the laser was rotated to different angles, its scanning plane could be still guaranteed level. The position of the crosshead of the laser pointer on the trunk needed to be marked with a marker pen. After leveling, the experiment software needed to be debugged to see if the data acquisition was normal. And the trees were required in the distance range of 50-3500mm from the laser. Among this range, trees were the only needed objects on the laser scanning plane. In outdoor experiment, unlike the indoor experiment, it is necessary to see whether the data collected fitted the actual situation or not. If there is abnormal situation, timely processing should be done. After debugging, true value of the tree needed being measured. Because the laser scanning plane was fixed and the facing direction of the tree towards the laser was fixed too, the true value could be obtained at any scanning angle. In this experiment, true value was got at the position where the laser pointer had been located at 90°. When measuring the actual diameter of the trunk, the actual measure position was at a distance of “S” below the laser scanning point. The value of “S” is given by the below formula:

S  tan   L

(3)

The measure was required to take three times at different directions on the surface of the tree, separated by 120°, the start position could be any, then got the average value. Original data file stored should be emptied when the formal experiment was about to carry out. Then the laser pointer was located 30°, so the scanning angle of the trunk was also 30°. The experiment was conducted according to the sequence of 30 °, 60 °, 90 °, 120 ° and 150 °, and scanning data was collected for three times at different frequency and the same angle, and then got the average. Frequency was set by increasing mode, and the values were 25, 35, 50, 75 and 100Hz in turn, respectively corresponding angle resolution were 0.1667 °, 0.25 °, 0.3333 °, 0.5 ° and 0.6667 °. The measured data got from the three algorithms was recorded to relevant txt files. This portion had a special data storage module. At the positions of 2 and 3m, it was also needed to repeat the experiment the same as the distance of 1m, to level before the experiment and to measure real data after leveling.

Results and Discussions The calibration experiment of laser pointer’s elevation The same experiment was repeated for three times. The results had little error, and then the average was received. It was:

  5.346 2014 ASABE – CSBE/SCGAB Annual International Meeting Paper

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The experiment to verify the accuracy of data collection at different angles There were two sets of data got from the experiment. One group was the measure data of software, and another was the data came from manual measure. As the final purpose of the experiment is to service for the targeted spray, the distance between the tree and laser and the diameter of the circle fitted were the mainly factors needed to consider in early study. This experiment was to verify that if the two factors were consistent when the same algorithm was used to the same tree at different angles and the same frequency. If the result was consistent, when data collection experiment for multiple trees was carried out, the influence of the angle value to the fitting results could be neglected; if not , another multiple sets of experiments needed to be taken to find out the law of errors. The actual measured value of the experiment is shown in table 1. The starting position of 0° could be selected at will, and there were three positions. Of any two positions, the distance was the same on the surface of the tree. Finally, the actual distance and diameter value were all known to us. Table 1. Actual measurement results at a distance of 1m. Actual data(mm) Position

Diameter

0°

242.0

120°

241.0

240°

239.0

Average of diameter

240.7

Average of radius

120.3

Actual distance

1120.3

Analysis of experimental data takes Kasa’s Method for example. It is shown in table 2 and 3, respectively express summary data analysis of trunk radius and distance at the position of 1m. Table 4 and 5 are the oneway ANOVA forms for the measurement value of radius and distance at the position of 1m. Take table 4 for example, the value of significance level α was set to 0.05 before the one-way ANOVA was done. The result shows the value of F is 15.33 which is greater than the critical value of it. Meanwhile, the value of P is 6.93E-06 which is much less than the significance level 0.05. The null hypothesis is rejected. The data obtained by these tables illustrates that when Kasa’s Method was used, the angle value significantly affected radius and distance. Similarly, the results of measurements with MLS Method and AI Method are also could be inferred from the data obtained. The result of MLS Method is consistent with the result of Kasa’s Method, but the AI Method have a different result, and the related data shows that the angle value doesn’t have a significant effect on measurement values. However, the sum of squares for error (SSE) of AI Method is greater than the other two algorithms, shown in table 6. Such result shows that AI Method isn’t stable enough in fitting and measuring. It also has great randomness. So the result of one-way ANOVA can’t conclude that the angle doesn’t have a significant effect on measurement values when using AI Method. And it could be considered to give up AI Method in subsequent experiments. This algorithm can be only used in certain ideal situations. Table 2. Summary data analysis of trunk’s radius using Kasa’s Method. (mm) Group

Observation numbers

Average

Variance

Row 1

5

111.2

1.84

Row 2

5

108.2

2.47

Row 3

5

106.5

0.79

Row 4

5

107.2

0.68

Row 5

5

106.3

0.72


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Table 3. Summary data analysis of trunk’s distance using Kasa’s Method. (mm)

Group

Observation numbers

Average

Variance

Row 1

5

1144.5

4.175

Row 2

5

1143.5

2.53

Row 3

5

1143.0

4.45

Row 4

5

1143.3

1.56

Row 5

5

1137.0

1.81

Table 4. The one-way ANOVA of trunk’s radius using Kasa’s Method. Differences source

SS

df

MS

F

P-value

F crit

SSA

79.84

4

19.96

15.33

6.93E-06

2.87

SSE

26.04

20

1.30

Sum

105.89

24

Table 5. The one-way ANOVA of trunk’s distance using Kasa’s Method. Differences source

SS

df

MS

F

P-value

F crit

SSA

182.01

4

45.50

15.65

5.93E-06

2.87

SSE

58.14

20

2.91

Sum

240.16

24

P-value

F crit

0.57

2.87

Table 6. The one-way ANOVA of trunk’s distance using AI Method. Differences source

SS

df

MS

F

SSA

6238.57

4

1559.64

0.74

SSE

41899.36

20

2094.97

Sum

48137.92

24

Conclusion The following conclusions can be drawn through analysis of the experiment. First, when the laser scanner LMS511 is started, the measured value doesn’t change significantly as the time elapses. The laser scanner doesn’t need preheating in practical use. If takes the problems such as starting of equipment and the power into account, the laser can be preheat for a short time. Second, although the elevation of laser pointer which was mounted on the holder of laser scanner can be measured, the method can only be used in ordinary experiment because the mounting was not strictly accurate. A more precise equipment is needed to fix the actual scanning plane in the experiment which requires a higher accuracy. Third, the results of the one-way ANOVA shows that Kasa’s Method and MLS Method have a significant effect on the measurement of the radius and distance of the trunk among the three algorithms. The data of AI method isn’t stable enough, so it should be considered to abandon this method when the actual experiment for multiple trees is about to be conducted. For the other two algorithms, the effect of measured angles to measurement value needs to be paid great attention to. If relatively accurate data was wanted, the best scanning angle should be chosen.

Acknowledgements This research was supported by the State Forestry Administration of China project to introduce advanced international forestry science and technology (Grant No. 20134-02), 2013 Technology Foundation for Selected Overseas Chinese Scholar, China Ministry of Personnel, and the Beijing Forestry University Young Scientist 2014 ASABE – CSBE/SCGAB Annual International Meeting Paper

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Fund (Grant No. BLX2011016).

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