Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection

2026-06-09

AI Summary

This work addresses the degraded state estimation accuracy of autonomous agricultural robots operating in unstructured outdoor environments, where GNSS, LiDAR, and visual systems often fail and high-frequency vibrations further compromise performance. To overcome these challenges, the authors propose a jerk-augmented extended Kalman filter (EKF) that enhances motion modeling by explicitly incorporating jerk as a state variable. Furthermore, a multi-tuning-factor (MTF) adaptive mechanism is introduced to dynamically adjust the measurement noise covariance in real time, thereby mitigating the impact of abrupt disturbances and sensor anomalies. Relying solely on inertial measurement unit (IMU) data, the proposed method enables robust dead reckoning and significantly reduces 3D position root-mean-square error (RMSE) in field tests with the Salin247 robot, substantially outperforming a baseline EKF under conditions devoid of external sensor inputs.

Abstract

Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high-frequency vibrations inherent in off-road environments. This paper proposes a robust navigation algorithm based on a jerk-augmented Extended Kalman Filter (EKF) integrated with a Multiple Tuning Factor (MTF) adaptation method. Unlike standard EKF approaches that assume constant measurement noise, our method dynamically adjusts the measurement covariance matrix in real-time, allowing the system to cope with sudden disturbances and sensor outliers. We evaluate the algorithm using real-world data from a Salin247 autonomous robot. Results demonstrate that jerk-augmentation combined with MTF adaptation significantly reduces 3D position Root Mean Square Error (RMSE) compared to baseline EKF models, providing superior dead-reckoning capabilities.

Problem

• resilient navigation
• autonomous farm robots
• sensor outages
• high-frequency vibrations
• state estimation

Innovation

• jerk-augmented EKF
• IMU-only navigation
• Multiple Tuning Factor (MTF)
• disturbance rejection
• autonomous farm robots