Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection

Who’s working on this project: Panda, S.K, Lee, Y., Khalid, M.K

The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground. We propose Agronav, an end-to-end vision-based autonomous navigation framework, which outputs the centerline from the input image by sequentially processing it through semantic segmentation and semantic line detection models. We also present Agroscapes, a pixel-level annotated dataset collected across six different crops, captured from varying heights and angles. This ensures that the framework trained on Agroscapes is generalizable across both ground and aerial robotic platforms. Codes, models and dataset will be released on GitHub.

Publication: Panda, S.K, Lee, Y., Khalid, M.K “Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection”

Funding: United States Department of Agriculture (USDA Award No. 2021- 67022-34200 & 2022-67022-37021) and National Science Foundation (NSF Award No. IIS-2047663 & CNS2213839)

Github: https://github.com/StructuresComp/agronav

Youtube: TBD