Drivable Area Estimation for Autonomous Agriculture Applications FEV North America Incorporated

Format:

Book

Conference/Event

Author/Creator:

Alzu'bi, Hamzeh, author.

Contributor:

Alrousan, Qusay

Tasky, Tom

Taylor, Elizabeth

Varasquim, Juliano

Conference Name:

WCX SAE World Congress Experience (2023-04-18 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

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 MATLAB®/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

Notes:

Vendor supplied data

Publisher Number:

2023-01-0054

Access Restriction:

Restricted for use by site license