用抖动增强模型提升农机导航抗干扰能力
Resilient Navigation for Autonomous Farm Robots by Leveraging Jerk-Augmented Models with IMU-Only Disturbance Rejection
- 引入抖动项与动态噪声调整，实时应对传感器失效和振动
- 实测3D定位均方根误差显著降低，优于传统滤波器
- 适合在无信号或颠簸环境中运行的农业机器人使用
自主农业机器人在非铺装环境下常因传感器故障（如GNSS、LiDAR、视觉）及高频振动导致状态估计失准。本文提出一种基于抖动增强型扩展卡尔曼滤波器（EKF）的鲁棒导航算法，结合多调谐因子（MTF）自适应机制。与传统EKF假设测量噪声恒定不同，该方法可实时动态调整测量协方差矩阵，有效应对突发扰动与传感器异常。利用Salin247机器人真实数据进行评估，结果表明，抖动增强结合MTF自适应显著降低了3D位置均方根误差（RMSE），大幅提升了惯性导航的死区追踪能力。
原文摘要 · Abstract (English)
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.
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