This paper proposes to use a newly-derived transformed inertial navigation system (INS) mechanization to fuse INS with other complimentary sensors. Through formulating the attitude, velocity and position as one group state of group of double direct spatial isometries, the transformed INS mechanization has proven to be group affine, which opens door to log-linearity and filtering consistency. In order to make use of the transformed INS mechanization in inertial based applications, both the right and left error state models are derived. The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial based applications. Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the SE2(3) based error state covariance and feedback correction corresponding to the error state definitions. Land vehicle experiments are conducted to evaluate the performance of the derived error state models. It is shown that the most striking superiority of using the derived error state models is their ability to handle the large initial attitude misalignments, which is just the result of log-linearity property of the derived error state models. Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications.
翻译:本文建议使用新产生的转换惯性导航系统(INS)机械化来将INS与其他辅助传感器结合。通过将姿态、速度和位置作为双直接空间异体组群的一组状态来形成,经过改造的INS机械化已被证明是组形的,从而打开了日志线性和过滤一致性的大门。为了在基于惯性的应用中使用经过改造的INS机械化,得出了中、左误差状态模型。IMS/GPS和INS/Odomel集成作为基于惯性的应用的两个代表进行调查。在两种应用中,通过将产生的误差状态模型的某些应用方面加以介绍,其中包括如何选择误差状态模型、基于SE2(3)的误差状态初始化和反馈校正与误差状态定义相对应的误差状态。为评价从出错误状态模型的性能最显著的优势是使用产生的误差状态模型,其处理大型初始姿态错位的优势是它们的能力,而惯性误差状态模型正是对正态性调整模型中产生的误差性,因此,因此,可以将产生的误差模型用于。