Conference PaperNeural-Kalman GNSS/INS Navigation for Precision AgricultureDu, Yayun; Saha, Swapnil Sayan; Sandha, Sandeep Singh; Lovekin, Arthur; Wu, Jason; Siddharth, S.; Chowdhary, Mahesh; Jawed, Mohammad Khalid; Srivastava, ManiPrecision 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 (1.4–5.8× better than competing techniques), while tracking within 2.75 m with 20 mins of GPS outage. Second, we introduce a user-friendly video-processing toolbox 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. The dataset, toolbox, and code are available at: https://github.com/nesl/agrobot.IEEE2023-05-29104947462023 IEEE International Conference on Robotics and Automation (ICRA)9622 to 9629979-8-3503-2365-8https://doi.org/10.1109/ICRA48891.2023.101613511705135London, United KingdomNational Science Foundation