Sains Malaysiana 50(11)(2021): 3193-3204
http://doi.org/10.17576/jsm-2021-5011-04
Extracting
Crown Morphology with a Low-Cost Mobile LiDAR Scanning System in the Natural
Environment
(Mengekstrak Morfologi Silara dengan Sistem Pengimbasan LiDAR Mudah Alih Kos Rendah pada Alam Sekitar Semula Jadi)
KAI WANG1, JUN ZHOU2*,
WENHAI ZHANG1 & BAOHUA ZHANG1
1College of Engineering, Nanjing
Agricultural University, Nanjing 210031, China
2Jiangsu Province Key Laboratory of Intelligent Agricultural
Equipment, Nanjing 210031, China
Diserahkan: 16 Ogos 2020/Diterima: 7 Mac 2021
ABSTRACT
To meet the demand for intelligent
measurements of canopy morphological parameters, a mobile LiDAR scanning system
with LiDAR and IMU as the main sensors was constructed. The system uses a
LiDAR-IMU tight coupling odometry method to reconstruct a point cloud map of
the area surveyed. After using the RANSAC algorithm to remove the map ground,
the European clustering algorithm is used for point cloud segmentation.
Finally, morphological parameters of the canopy, such as crown height, crown
diameter, and crown volume, are extracted using statistical and voxel methods.
To verify the algorithm, a total of 43 trees in multiple plots of the campus
were tested and compared. The algorithm defined in this study was evaluated
with manual measurements as reference, and the morphological parameters of the
canopy obtained using the LOAM and LeGO-LOAM
algorithms as the basic framework were compared. Experiments show that this
method can be used to easily obtain the crown height, crown diameter, and crown
volume of the area; the correlation coefficients of these parameters were 0.91,
0.87, and 0.83, respectively. Compared with the LOAM and LeGO-LOAM
methods, they were increased by 0.004, 0.12, and 0.13 and 0.07, 0.15, and 0.04,
respectively. The test results for this new system are positive and meet the
requirements of horticulture and orchard measurements, indicating that it will
have significant value as an application.
Keywords: Canopy measurement; mobile
LiDAR scanning system; point cloud segmentation; tightly-coupled odometry
ABSTRAK
Bagi memenuhi permintaan untuk pengukuran parameter morfologi kanopi yang cerdas, sistem pengimbasan LiDAR mudah alih dengan LiDAR dan IMU sebagai sensor utama telah dibina. Sistem ini menggunakan kaedah odometri gandingan erat LiDAR-IMU untuk menyusun semula peta awan titik kawasan yang dikaji. Setelah menggunakan algoritma RANSAC untuk menghilangkan landasan peta, algoritma pengelompokan Eropah digunakan untuk segmentasi awan titik. Akhirnya,
parameter morfologi kanopi, seperti tinggi silara, diameter silara dan isi padu silara, diekstrak menggunakan kaedah statistik dan voksel. Untuk mengesahkan algoritma, sebanyak 43 pokok di beberapa petak kampus diuji dan dibandingkan. Algoritma yang ditentukan dalam kajian ini dinilai dengan pengukuran manual sebagai rujukan dan parameter morfologi kanopi yang diperoleh menggunakan algoritma LOAM dan LeGO-LOAM sebagai kerangka asas dibandingkan. Uji kaji menunjukkan bahawa kaedah ini dapat digunakan untuk mendapatkan ketinggian silara, diameter silara dan isi padu silara dengan mudah; pekali korelasi parameter ini masing-masing 0.91, 0.87 dan 0.83. Berbanding dengan kaedah LOAM dan LeGO-LOAM, mereka masing-masing dinaikkan sebanyak 0.004, 0.12 dan 0.13 dan 0.07, 0.15 dan 0.04.
Hasil ujian untuk sistem baru ini adalah positif dan memenuhi syarat pengukuran hortikultur dan kebun yang menunjukkan bahawa ia akan memiliki nilai yang signifikan sebagai aplikasi.
Kata kunci: Odometri gandingan erat; pengukuran kanopi; sistem pengimbasan LiDAR mudah alih; titik segmentasi awan
RUJUKAN
Agarwal, S., Mierle, K. &
Others. 2018. Ceres Solver. http://ceres-solver.org/.
Davison, S., Donoghue, D.N.M. & Galiatsatos,
N. 2020. The effect of leaf-on and leaf-off forest canopy conditions on LiDAR
derived estimations of forest structural diversity. International Journal of Applied Earth Observation and Geoinformation 92: 102160.
Dong, Y., Li, Y., Li, Y., Li, P. & L, Y. 2018. Tree
crown outline point extracting and volume calculation based on improved convex
hull algorithm. Engineering of Surveying
and Mapping 27: 66-71.
Escolà, A., Martínez-Casasnovas,
J.A., Rufat, J., Arnó, J., Arbonés, A., Sebé, F., Pascual,
M., Gregorio, E. & Rosell-Polo, J.R. 2016. Mobile
terrestrial laser scanner applications in precision fruticulture/horticulture
and tools to extract information from canopy point clouds. Precision Agriculture 18: 111-132.
Fan, Z., Feng, Z., Zheng, J., Fan, J., Yan, F. & Qiu, Z. 2015. Tree crown volume calculation and prediction
model establishment using cubic lattice method. Transactions of the Chinese Society for Agricultural Machinery 46(3): 321-327.
Hadas, E., Jozkow, G., Walicka, A. & Borkowski, A. 2019. Apple orchard
inventory with a LiDAR equipped unmanned aerial system. International Journal of Applied Earth Observation and Geoinformation 82: 101911.
Langning, H. & Xiaoli,
Z. 2019. A new method of equiangular sectorial voxelization of single-scan
terrestrial laser scanning data and its applications in forest defoliation
estimation. ISPRS Journal of
Photogrammetry and Remote Sensing 151: 302-312.
Li, Q., Zheng, J., Zhou, H., Zhang, H., Shu, Y. & Xu, B.
2016. Online measurement of tree canopy volume using vehicle-borne 2-D laser
scanning. Transactions of the Chinese
Society for Agricultural Machinery 47: 310-314.
Pedersen, K., Hulusic, V., Amelidis, P. & Slattery, T. 2020. Spatialised audio in a custom-built openGL-based ear training
virtual environment. IEEE Computer
Graphics and Applications 4(5): 67-81.
Pfeiffer, A.S., Guevara, J., Cheein,
F.A. & Sanz, R. 2018. Mechatronic terrestrial LiDAR for canopy porosity and
crown surface estimation. Computers and
Electronics in Agriculture 146: 104-113.
Pierzchała, M., Giguère,
P. & Astrup, R. 2018. Mapping forests using an
unmanned ground vehicle with 3D LiDAR and graph-SLAM. Computers and Electronics in Agriculture 145: 217-225.
Qian, X. & Ye, C. 2014. NCC-RANSAC: A fast plane
extraction method for 3-D range data segmentation. IEEE Trans 44(12): 2771-2783.
Qin, T., Li, P. & Shen, S. 2018. Vins-mono:
A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics 34(4):
1004-1020.
Qin, C., Ye, H., Pranata, C.E.,
Han, J., Zhang, S. & Liu, M. 2020. RLINS: A robocentric lidar-inertial state estimator for robust and efficient navigation. 2020 IEEE International Conference on
Robotics and Automation (ICRA). pp. 8899-8906.
Rahul, K., Raheman, H. & Paradkar, V. 2020. Design of a 4 DOF parallel robot arm and
the firmware implementation on embedded system to transplant pot seedlings. Artificial Intelligence in Agriculture 4:
172-183.
Rusu, R.B. & Cousins, S. 2011. Point cloud library (pcl). Proceedings of
the 2011 IEEE International Conference on Robotics and Automation. pp. 1-4.
Segal, A., Haehnel, D. & Thru,
S. 2009. Generalized-ICP. Proceedings of
Robotics: Science and Systems 2(4): 435.
Seka, D., Bonny, B.S., Yoboué,
A.N., Sié, S.R. & Adopo-Gourène,
B.A. 2019. Identification of maize (Zea mays L.)
progeny genotypes based on two probabilistic approaches: Logistic regression
and naïve Bayes. Artificial
Intelligence in Agriculture 1: 9-13.
Shan, T. & Englot, B. 2018. LeGO-LOAM: Lightweight and ground optimized lidar odometry
and mapping on variable terrain. IEEE/RSJ
International Conference on Intelligent Robots and Systems. pp. 4758-4765.
Sun, G., Wang, X., Ding, Y., Lu, W. & Sun, Y. 2019.
Remote measurement of apple orchard canopy information using unmanned aerial
vehicle photogrammetry. Agronomy 9(11): 744.
Underwood, J.P., Hung, C., Whelan, B. & Sukkarieh, S. 2016. Mapping almond orchard canopy volume,
flowers, fruit and yield using lidar and vision sensors. Computers and Electronics in Agriculture 130: 83-96.
Wang, J., Zhang, F., Gao, H. & Lu, C. 2018. Extracting
crown structure parameters of individual tree by using ground-based laser
scanner. Transactions of the Chinese
Society for Agricultural Machinery 49: 200-206.
Ye, H., Chen, Y. & Liu, M. 2019. Tightly coupled 3D
lidar inertial odometry and mapping. International
Conference on Robotics and Automation (ICRA). pp. 3144-3150.
Zhang, J. & Singh, S. 2016. Low-drift and real-time
lidar odometry and mapping. Autonomous
Robots 41: 401-416.
Zhang, J. & Singh, S. 2014. LOAM: Lidar odometry and
mapping in real-time. Proceedings of the
Robotics: Science and Systems 2(9).
Zhou, S., Kang, F., Li, W., Kan, J., Zheng, Y. & He, G.
2019. Extracting diameter at breast height with a handheld mobile LiDAR system
in an outdoor environment. Sensors
(Basel) 19(14): 3212.
*Pengarang untuk surat-menyurat;
email: zhoujun@njau.edu.cn
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