Neural-Kalman GNSS/INS Navigation for Precision Agriculture

Who’s working on this: Du, Y., Saha, S.S., Sandha, S.S., Lovekin, A., Wu, J., Siddharth, S., Chowdhary, M.

Precision agricultural robots require high-resolution navigation solutions. In this paper, we introduce a robust neural-inertial sequence learning approach to track such robots with ultra-intermittent GNSS updates. First, we propose an ultra-lightweight neural-Kalman filter that can track agricultural robots within 1.4 m (1.4 - 5.8x better than competing techniques), while tracking within 2.75 m with 20 mins of GPS outage. Second, we introduce a user-friendly video-processing pipeline to generate high-resolution (+- 5 cm) position data for fine-tuning pre-trained neural-inertial models in the field. Third, we introduce the first and largest (6.5 hours, 4.5 km, 3 phases) public neural-inertial navigation dataset for precision agricultural robots.

Publication: Du, Y., Saha, S.S., Sandha, S.S., Lovekin, A., Wu, J., Siddharth, S., Chowdhary, M., Jawed, M.K., Srivastava, M., “Neural-Kalman GNSS/INS Navigation for Precision Agriculture”, IEEE International Conference on Robotics and Automation (ICRA) 2023

Funding: Special thanks to Matt Conroy and Andre Biscaro from UCANR for their field preparation. This work was supported in part by the CONIX Research Center, one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA; by the IoBT REIGN Collaborative Research Alliance funded by the Army Research Laboratory (ARL) under Cooperative Agreement W911NF-17-2-0196; by the NIH mHealth Center for Discovery, Optimization and Translation of Temporally-Precise Interventions (mDOT) under award 1P41EB028242; by the National Science Foundation (NSF) under awards OAC-1640813, CNS-1822935, IIS-1925360, CNS-2213839, and CMMI-2047663; by the King Abdullah University of Science and Technology (KAUST) through its Sensor Innovation research program; and by the National Institute of Food and Agriculture, USDA under awards 2021-67022-342000 and 2021-67022-34200.

YouTube: https://youtu.be/9e3Q_9aTCQ4

GitHub: https://github.com/nesl/agrobot