Jingyu Liu
ABSTRACT
We present a containerized mobile sensing simulation (CMOS) framework developed for smart agriculture applications. This framework includes 1) 3D environment and object modeling, 2) mobile platform motion planning and control, and 3) optical sensing simulation, all implemented and connected within containers. Specifically, we build a user-friendly interface for 3D modeling, e.g., cornfield modeling using Blender. We use an unmanned aerial vehicle (UAV) as our mobile sensing platform and integrate UAV 3D model, flight path planning and control with robot operating system (ROS) packages and the Gazebo simulator. We also implemented optical sensing, e.g., collecting RGB image data from cameras in our simulation framework. This framework can be used not only in leaf area index correction and other analytical support for agriculture operations, but also as a synthetic data annotation tool for leaf segmentation and other smart agriculture applications. We demonstrate the major components of the CMOS framework, and how to use it to automatically annotate image data for the leaf segmentation application.
- CMOS Framework Github repository. https://github.com/Vieloooo/CMOS, 2022.Google Scholar
- A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun. CARLA: An open urban driving simulator. In 1st Annual Conference on Robot Learning, 2017.Google Scholar
- G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez. The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In 2016 IEEE CVPR, pages 3234--3243, 2016.Google ScholarCross Ref
Index Terms
- Containerized Mobile Sensing Simulation Framework for Smart Agriculture
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