3D-Simulation Data-Making Trial to present and analyze Small-sized Farmlands Fields with Car-shaped Robot, ROS2, SLAM and Foxy for Real agricultural workers

In this study, we create various application systems focusing on agricultural (agri-) field data digitalization issues that will benefit traditional agri-researchers, workers, and their respective managers. We obtain three-dimensional (3D) information on agri-environments (e.g., rice fields, farmlands) via roaming robots with sensors. Robot-controlled middleware, such as robot operating systems (ROS), are often used for such robots. Thus, we selected car-shaped robot (NANO-RT1), ROS2, and the SLAM-based system. The car-shaped robot-based system operates sensor units uniformly. With this technology, we can recognize our location at an unknown place, and the robot can run. There are challenges in accurately presenting quantitative accuracy data for this type of study. We address this by providing average and standard deviation (SD) data for certain situations using five algorithms: (1) Hector-SLAM, (2) G-mapping, (3) Karto-SLAM, (4) Core-SLAM, and (5) Lago-SLAM. We believe the proposed holistic system has the potential to improve not only agri-businesses, but also agri-skills and overall security levels.