Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation
Introduction Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments a...
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| Published in: | Precision agriculture Vol. 25; no. 6; pp. 2881 - 2902 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01-12-2024
Springer Nature B.V |
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| Abstract | Introduction
Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras.
Materials and methods
In this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms.
Results and conclusion
The high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics. |
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| AbstractList | Introduction
Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras.
Materials and methods
In this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms.
Results and conclusion
The high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics. IntroductionService robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras.Materials and methodsIn this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms.Results and conclusionThe high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics. |
| Author | Navone, Alessandro Tuberga, Brenno Chiaberge, Marcello Ambrosio, Marco Martini, Mauro |
| Author_xml | – sequence: 1 givenname: Mauro orcidid: 0000-0002-6204-3845 surname: Martini fullname: Martini, Mauro email: mauro.martini@polito.it organization: Department of Electronics and Telecommunications, Politecnico di Torino – sequence: 2 givenname: Marco surname: Ambrosio fullname: Ambrosio, Marco organization: Department of Electronics and Telecommunications, Politecnico di Torino – sequence: 3 givenname: Alessandro surname: Navone fullname: Navone, Alessandro organization: Department of Electronics and Telecommunications, Politecnico di Torino – sequence: 4 givenname: Brenno surname: Tuberga fullname: Tuberga, Brenno organization: Department of Electronics and Telecommunications, Politecnico di Torino – sequence: 5 givenname: Marcello surname: Chiaberge fullname: Chiaberge, Marcello organization: Department of Electronics and Telecommunications, Politecnico di Torino |
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| Cites_doi | 10.1016/j.compag.2018.01.009 10.1016/j.inpa.2023.02.001 10.3920/978-90-8686-947-3 10.26599/TST.2021.9010012 10.1016/j.compag.2017.12.001 10.1109/CVPR.2016.350 10.1109/LRA.2020.2965061 10.1016/j.patcog.2016.01.013 10.4060/cc0959en 10.3920/978-90-8686-947-3_56 10.1016/j.compag.2020.105812 10.3390/s19173796 10.1109/MetroAgriFor.2019.8909219 10.1109/IROS.2004.1389727 10.1109/CASE49997.2022.9926582 10.1038/s43016-021-00322-9 10.1109/ICCCR56747.2023.10193996 10.1016/j.eaef.2015.09.002 10.1007/978-3-030-73050-5_16 10.1109/IROS55552.2023.10341261 10.1109/IROS51168.2021.9635969 10.1016/j.compag.2021.106418 10.1016/j.compag.2020.105256 10.3389/fpls.2021.684328 10.1109/CVPR.2018.00474 10.1109/ICACCS.2019.8728415 10.1109/ACCESS.2020.2964608 10.1007/978-3-031-26422-1_13 10.1016/j.compag.2022.106977 10.1016/j.compag.2022.107593 10.1016/j.engappai.2018.09.011 10.1007/s10846-022-01793-z 10.1109/CVPR.2018.00745 10.22214/ijraset.2018.1146 10.3389/frai.2023.1213330 10.1109/ICCV.2019.00140 10.1109/ICCV51070.2023.00371 10.5935/1806-6690.20200084 10.1109/ACCESS.2020.3021739 10.1109/ECMR59166.2023.10256334 10.1023/A:1015678121948 |
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| Keywords | Service robotics Autonomous navigation Synthetic data Deep semantic segmentation |
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| Snippet | Introduction
Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions.... IntroductionService robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions.... |
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| SubjectTerms | Agriculture Atmospheric Sciences Autonomous navigation Biomedical and Life Sciences Chemistry and Earth Sciences Computer Science Crops Datasets Image processing Image quality Image segmentation Life Sciences Physics Precision farming Remote Sensing/Photogrammetry Robot control Robotics Robust control Semantic segmentation Soil Science & Conservation Statistics for Engineering Synthetic data Virtual networks Visual control Visual fields Visual perception Visual perception driven algorithms |
| Title | Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation |
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