Neural-Kalman GNSS/INS Navigation for Precision Agriculture
Yayun Du, Swapnil Sayan Saha, Sandeep Singh Sandha, Arthur Lovekin, Jason Wu, S. Siddharth, Mahesh Chowdhary, Mohammad Khalid Jawed
Abstract
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 (1.4–5.8× 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.
BibTeX
@inproceedings{icra2023_neuralkalmangnss,
title = {Neural-Kalman GNSS/INS Navigation for Precision Agriculture},
author = {Yayun Du and Swapnil Sayan Saha and Sandeep Singh Sandha and Arthur Lovekin and Jason Wu and S. Siddharth and Mahesh Chowdhary and Mohammad Khalid Jawed and Mani Srivastava},
booktitle = {ICRA 2023},
year = {2023}
}