Robust state estimation for Micro Aerial Vehicles based on system dynamics
Robust state estimation for Micro Aerial Vehicles based on system dynamics
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DOI：
10.1109/icra.2015.7139935
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
M. Burri;Manuel Datwiler;Markus Achtelik;R. Siegwart
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文献类型：
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作者：
M. Burri;Manuel Datwiler;Markus Achtelik;R. Siegwart
在这项工作中，我们提出了一个基于模型的估计方案的多旋翼微型飞行器（MAVs）。虽然在过去已经提出了微型飞行器的建模方法，这些模型很少被用于实时状态估计的微型飞行器机载。在这项工作的基础上，我们确定了最主要的影响，并提出了一个易于使用的校准方案，估计模型参数。给定这些参数的校准估计，我们推导出一个状态估计器，其中间接扩展卡尔曼滤波器（EKF）的状态预测是由MAV模型驱动的。仅使用来自惯性测量单元（IMU）和气压传感器的测量值-几乎每个MAV都可用-我们基于模型的公式保持MAV的估计速度在所有方向上都有界，而不是最先进的IMU模型驱动的状态估计器机载MAV。这对于保持MAV安全地在空中飞行是至关重要的，例如在基于视觉的定位系统发生故障或重新初始化的情况下。
In this work, we present a model-based estimation scheme for multi-rotor Micro Aerial Vehicles (MAVs). Although modeling approaches for MAVs have been presented in the past, these models have rarely been used for real-time state estimation onboard MAVs. Building on this work, we identify the most dominant effects and propose an easy-to-use calibration scheme for estimation of the model parameters. Given the calibration estimates for these parameters, we derive a state estimator where the state prediction of the indirect Extended Kalman Filter (EKF) is driven by a MAV model. Solely using measurements from the Inertial Measurement Unit (IMU) and a barometric pressure sensor - both available on almost every MAV - our model-based formulation keeps the estimated velocity of the MAV bounded in all directions, as opposed to state of the art IMU-model driven state estimators onboard MAVs. This is crucial for keeping MAVs airborne safely, for instance in the case of failures or re-initialization of vision based localization systems.