Towards Autonomous Visual Navigation in Arable Fields
Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application. Many current approaches rely on accurate GPS, however, such technology is expensive and can be impacted by lack of coverage. As such, autonomous nav...
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| Vydané v: | Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems s. 6585 - 6592 |
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IEEE
23.10.2022
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| ISSN: | 2153-0866 |
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| Abstract | Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application. Many current approaches rely on accurate GPS, however, such technology is expensive and can be impacted by lack of coverage. As such, autonomous navigation through sensors that can interpret their environment (such as cameras) is important to achieve the goal of autonomy in agriculture. In this paper, we introduce a purely vision-based navigation scheme that is able to reliably guide the robot through row-crop fields using computer vision and signal processing techniques without manual intervention. Independent of any global localization or mapping, this approach is able to accurately follow the crop-rows and switch between the rows, only using onboard cameras. The proposed navigation scheme can be deployed in a wide range of fields with different canopy shapes in various growth stages, creating a crop agnostic navigation approach. This was completed under various illumination conditions using simulated and real fields where we achieve an average navigation accuracy of 3.82cm with minimal human intervention (hyper-parameter tuning) on BonnBot-I. |
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| AbstractList | Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application. Many current approaches rely on accurate GPS, however, such technology is expensive and can be impacted by lack of coverage. As such, autonomous navigation through sensors that can interpret their environment (such as cameras) is important to achieve the goal of autonomy in agriculture. In this paper, we introduce a purely vision-based navigation scheme that is able to reliably guide the robot through row-crop fields using computer vision and signal processing techniques without manual intervention. Independent of any global localization or mapping, this approach is able to accurately follow the crop-rows and switch between the rows, only using onboard cameras. The proposed navigation scheme can be deployed in a wide range of fields with different canopy shapes in various growth stages, creating a crop agnostic navigation approach. This was completed under various illumination conditions using simulated and real fields where we achieve an average navigation accuracy of 3.82cm with minimal human intervention (hyper-parameter tuning) on BonnBot-I. |
| Author | Ahmadi, Alireza McCool, Chris Halstead, Michael |
| Author_xml | – sequence: 1 givenname: Alireza surname: Ahmadi fullname: Ahmadi, Alireza email: alireza.ahmadi@uni-bonn.de organization: University of Bonn,Bonn,Germany,53115 – sequence: 2 givenname: Michael surname: Halstead fullname: Halstead, Michael email: michael.halstead@uni-bonn.de organization: University of Bonn,Bonn,Germany,53115 – sequence: 3 givenname: Chris surname: McCool fullname: McCool, Chris email: cmccool@uni-bonn.de organization: University of Bonn,Bonn,Germany,53115 |
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| Snippet | Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application. Many... |
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| SubjectTerms | Agricultural Automation Cameras Crops Navigation Robot vision systems Robotics and Automation in Agriculture and Forestry Shape Switches Task analysis Vision-Based Navigation |
| Title | Towards Autonomous Visual Navigation in Arable Fields |
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