逆机械化教程

David Woodburn
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引用次数: 0

摘要

逆机械化将位置、速度和姿态(位姿)数据转换为惯性测量单元传感器数据(比力和旋转速率)。它消除了昂贵的真实飞行的需要,只是为了获得合理的惯性导航模拟传感器记录。当真实姿态数据可用,但不包括惯性传感器数据时,这可能是有用的。实际上,姿势数据本身可以是合成的。然后,研究人员可以利用这些估计的传感器数据向前机械化并获得与原始姿态数据精确匹配的姿态数据。生成传感器数据后,可以添加模拟传感器噪声以提高真实感,但重要的是,由于缺乏对偶性,反向和正向机械化算法本身不会添加任何额外的噪声;它们应该彼此完全一致。本教程详细介绍了逆机械化和正向机械化的一组方程。给出了如何从位置信息中计算速度信息,以及如何从速度中估计姿态信息。为了证明方程的准确性,将真实世界的姿态和传感器数据用作算法的输入,并对输出进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tutorial on Inverse Mechanization
Inverse mechanization converts position, velocity, and attitude (pose) data into inertial measurement unit sensor data (specific forces and rotation rates). It removes the need for expensive, real-world flights just to get reasonable sensor recordings for inertial navigation simulations. This can be helpful when real pose data is available but no inertial sensor data is included. Actually, the pose data itself could be synthetic. The researcher can then use this estimated sensor data to forward mechanize and get pose data, which should exactly match the original pose data. After generating the sensor data, simulated sensor noise could be added to improve realism, but it is essential that the inverse and forward mechanization algorithms themselves do not add any additional noise because of a lack of duality; they should be perfectly consistent with each other. This tutorial details the set of equations for inverse and forward mechanization. It also shows how to calculate velocity information from position information and how to estimate attitude information from velocities. As a demonstration of the accuracy of the equations, real-world pose and sensor data are used as inputs to the algorithms and the outputs are compared.
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