基于模型的四旋翼飞行器轨迹确定约束:直接定向的设计、标定与优点

Kenneth Joseph Paul, Davide Antonio Cucci, Jan Skaloud
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引用次数: 1

摘要

本文提出了一种新的方法,通过改进飞行器动力学模型的轨迹估计来提高机载激光扫描的地理参考。在车辆动力学模型(VDM)中,平台动力学和控制输入之间的关系被用作传感器融合的附加观测。这种关系适用于大多数平台,并且可以在不需要额外硬件的情况下使用。然而,这种关系是使用先验未知的参数来建模的。与模拟中使用的值相比,所提出的飞行中校准方法可以在估计的模型参数中实现小于2%的误差。进一步研究了惯性测量单元(IMU)噪声对机载激光扫描精度的影响,以证明当使用VDM测量时,可以降低地理参考点的位置误差。通过一个开源自动驾驶仪的蒙特卡罗模拟对结果进行了评估。由于车辆建模导致的估计姿态误差的减少随着时间相关IMU噪声的强度越高而增加。当采用VDM测量时,使用更高质量的惯性传感器不会导致地理参考点的位置误差的改善;然而,质量较低的惯性测量单元,例如自动驾驶仪上的惯性测量装置,显示地理参考点的位置误差的平均值和标准偏差分别降低了33%和46%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model-based constraints for trajectory determination of quad-copters: Design, calibration & merits for direct orientation

This paper proposes a novel method to improve georeferencing of airborne laser scanning by improved trajectory estimation using Vehicle Dynamic Model. In Vehicle Dynamic Model (VDM), the relationship between the dynamics of the platform and control inputs is used as additional observations for sensor fusion. This relationship is available for most platforms and can be used without the need for additional hardware. However, this relationship is modeled using parameters that are a priori unknown. The proposed in-flight calibration methodology can achieve less than 2% error in the estimated model parameters compared to the values used in simulation. The effect of Inertial Measurement Unit (IMU) noise on the accuracy of airborne laser scanning is further investigated to demonstrate the reduction in the position error of georeferenced points when VDM measurements are used. The results are evaluated through a Monte-Carlo simulation involving an open-source autopilot. The reduction in the error of the estimated attitude due to vehicle modeling increases with the higher intensity of time-correlated IMU noise. Using a higher quality inertial sensor does not lead to an improvement in the position error of georeferenced points when VDM measurements are employed; however, a lower quality Inertial Measurement Unit, such as those on an autopilot, shows a 33% and 46% reduction in the mean and standard deviation of the position error of the georeferenced points, respectively.

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