IMUのみの外乱除去とジャーク拡張モデルを活用した自律型農業ロボットの頑健なナビゲーション
Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection
GNSSやLiDARなどのセンサが途切れる環境でも、ジャーク拡張EKFと適応型ノイズ調整によりロバストな自己位置推定を実現する農業ロボット向けナビゲーション手法を提案した。
著者: Batu Candan, Mohammed Atallah, Simone Servadio, Saeed Arabi
分類: cs.RO, eess.SY
原文アブストラクト
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.