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Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera.
Sensors (Basel). 2020 Jul 07; 20(13)S

Abstract

In this study, an image registration algorithm was applied to calculate the rotation angle of objects when matching images. Some commonly used image feature detection algorithms such as features from accelerated segment test (FAST), speeded up robust features (SURF) and maximally stable extremal regions (MSER) algorithms were chosen as feature extraction components. Comparing the running time and accuracy, the image registration algorithm based on SURF has better performance than the other algorithms. Accurately obtaining the roll angle is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. To acquire the roll angle of agriculture machinery, a roll angle acquisition model based on the image registration algorithm was built. Then, the performance of the model with a monocular camera was tested in the field. The field test showed that the average error of the rolling angle was 0.61°, while the minimum error was 0.08°. The field test indicated that the model could accurately obtain the attitude change trend of agricultural machinery when it was working in irregular farmlands. The model described in this paper could provide a foundation for agricultural equipment navigation and autonomous driving.

Authors+Show Affiliations

Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China. Graduate School of Agriculture, Kyoto University, Kyoto 6068502, Japan.Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.Graduate School of Agriculture, Kyoto University, Kyoto 6068502, Japan.Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32645960

Citation

Li, Yang, et al. "Feature Point Registration Model of Farmland Surface and Its Application Based On a Monocular Camera." Sensors (Basel, Switzerland), vol. 20, no. 13, 2020.
Li Y, Huang D, Qi J, et al. Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera. Sensors (Basel). 2020;20(13).
Li, Y., Huang, D., Qi, J., Chen, S., Sun, H., Liu, H., & Jia, H. (2020). Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera. Sensors (Basel, Switzerland), 20(13). https://doi.org/10.3390/s20133799
Li Y, et al. Feature Point Registration Model of Farmland Surface and Its Application Based On a Monocular Camera. Sensors (Basel). 2020 Jul 7;20(13) PubMed PMID: 32645960.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera. AU - Li,Yang, AU - Huang,Dongyan, AU - Qi,Jiangtao, AU - Chen,Sikai, AU - Sun,Huibin, AU - Liu,Huili, AU - Jia,Honglei, Y1 - 2020/07/07/ PY - 2020/06/12/received PY - 2020/07/03/revised PY - 2020/07/06/accepted PY - 2020/7/11/entrez PY - 2020/7/11/pubmed PY - 2020/7/11/medline KW - attitude perception KW - farmland surface KW - feature point registration KW - monocular camera KW - robot vision JF - Sensors (Basel, Switzerland) JO - Sensors (Basel) VL - 20 IS - 13 N2 - In this study, an image registration algorithm was applied to calculate the rotation angle of objects when matching images. Some commonly used image feature detection algorithms such as features from accelerated segment test (FAST), speeded up robust features (SURF) and maximally stable extremal regions (MSER) algorithms were chosen as feature extraction components. Comparing the running time and accuracy, the image registration algorithm based on SURF has better performance than the other algorithms. Accurately obtaining the roll angle is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. To acquire the roll angle of agriculture machinery, a roll angle acquisition model based on the image registration algorithm was built. Then, the performance of the model with a monocular camera was tested in the field. The field test showed that the average error of the rolling angle was 0.61°, while the minimum error was 0.08°. The field test indicated that the model could accurately obtain the attitude change trend of agricultural machinery when it was working in irregular farmlands. The model described in this paper could provide a foundation for agricultural equipment navigation and autonomous driving. SN - 1424-8220 UR - https://www.unboundmedicine.com/medline/citation/32645960/Feature_Point_Registration_Model_of_Farmland_Surface_and_Its_Application_Based_on_a_Monocular_Camera L2 - https://www.mdpi.com/resolver?pii=s20133799 DB - PRIME DP - Unbound Medicine ER -
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