AMO-HEAD: 磁気外乱下におけるUAV向け適応型MARGのみの方位推定
AMO-HEAD: Adaptive MARG-Only Heading Estimation for UAVs under Magnetic Disturbances
屋内環境の磁気外乱に強いUAV方位推定のため、ジャイロ・加速度・磁気センサのみを用いた軽量EKFフレームワークを提案し、適応的プロセスノイズと磁気偏差検出による補正で精度を向上させた。
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著者: Qizhi Guo, Siyuan Yang, Junning Lyu, Jianjun Sun, Defu Lin, Shaoming He
分類: cs.RO
原文アブストラクト
Accurate and robust heading estimation is crucial for unmanned aerial vehicles (UAVs) when conducting indoor inspection tasks. However, the cluttered nature of indoor environments often introduces severe magnetic disturbances, which can significantly degrade heading accuracy. To address this challenge, this paper presents an Adaptive MARG-Only Heading (AMO-HEAD) estimation approach for UAVs operating in magnetically disturbed environments. AMO-HEAD is a lightweight and computationally efficient Extended Kalman Filter (EKF) framework that leverages inertial and magnetic sensors to achieve reliable heading estimation. In the proposed approach, gyroscope angular rate measurements are integrated to propagate the quaternion state, which is subsequently corrected using accelerometer and magnetometer data. The corrected quaternion is then used to compute the UAV's heading. An adaptive process noise covariance method is introduced to model and compensate for gyroscope measurement noise, bias drift, and discretization errors arising from the Euler method integration. To mitigate the effects of external magnetic disturbances, a scaling factor is applied based on real-time magnetic deviation detection. A theoretical observability analysis of the proposed AMO-HEAD is performed using the Lie derivative. Extensive experiments were conducted in real world indoor environments with customized UAV platforms. The results demonstrate the effectiveness of the proposed algorithm in providing precise heading estimation under magnetically disturbed conditions.
