This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Drivable Area Estimation for Autonomous Agriculture Applications
Technical Paper
2023-01-0054
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Autonomous farming has gained a vast interest due to the need for increased farming efficiency and productivity as well as reducing operating cost. Technological advancement enabled the development of Autonomous Driving (AD) features in unstructured environments such as farms. This paper discusses an approach of utilizing satellite images to estimate the drivable areas of agriculture fields with the aid of LiDAR sensor data to provide the necessary information for the vehicle to navigate autonomously. The images are used to detect the field boundaries while the LiDAR sensor detects the obstacles that the vehicle encounters during the autonomous driving as well as its type. These detections are fused with the information from the satellite images to help the path planning and control algorithms in making safe maneuvers. The image and point cloud processing algorithms were developed in MATLABĀ®/C++ software and implemented within the Robot Operating System (ROS) middleware. Test results show that the presented approach was implemented successfully with robust detection of drivable areas in a farm environment.
Authors
Topic
Citation
Alzu'bi, H., Varasquim, J., Taylor, E., Alrousan, Q. et al., "Drivable Area Estimation for Autonomous Agriculture Applications," SAE Technical Paper 2023-01-0054, 2023, https://doi.org/10.4271/2023-01-0054.Also In
References
- Cho , S.I. , Ki , N.H. , Lee , J.H. , and Choi , C.H. 1996
- Global Industry Analysis, Inc. 2020
- Isack , H. and Boykov , Y. Energy-Based Geometric Multi-Model Fitting Int J Comput vis 97 2012 123 147 https://doi.org/10.1007/s11263-011-0474-7
- Zangina , U. , Buyamin , S. , Aman , M.N. , Zainal Abidin , M.S. et al. Autonomous Mobility of a Fleet of Vehicles for Precision Pesticide Application Computers and Electronics in Agriculture
- USDA Farm Demographics - U.S. Farmers by Gender, Age, Race, Ethnicity, and More United States Department of Agriculture, National Agricultural Statistics Service 2019
- Morio , Y. , Hanada , Y. , Sawada , Y. , and Murakami , K. Field Scene Recognition for Self-Localization of Autonomous Agricultural Vehicle, Engineering in Agriculture Environment and Food
- https://earth.google.com/web/