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Information-Based Georeferencing by Dual State Iterated Extended Kalman Filter with Implicit Measurement Equations and Nonlinear Geometrical Constraints

机译：含隐式测量方程和非线性几何约束的双状态迭代扩展卡尔曼滤波器的基于信息的地理配准

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摘要

Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most accurate way possible. The easiest and most straightforward way for this purpose is to rely on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, at indoor environments or crowded inner-city areas, such data are not accurate to be entirely relied on. Therefore, appropriate filtering algorithms are required to compensate for possible errors and to improve the accuracy of the results. Sometimes it is also possible to increase the functionality of a filtering technique by engaging additional complementary information that can directly influence the outputs. Such information could be, e.g. geometrical features of the environment in which the MSS runs through. The current paper deals with MSS georeferencing by means of a Dual State Iterated Extended Kalman Filter (DSIEKF) that is based on an efficient combination of the Iterated Extended Kalman Filter (IEKF) with implicit measurement equations technique and nonlinear geometrical constraints. Final results of such an algorithm are shown to be satisfactory not only from the accuracy point of view but also the computation time.
机译：多传感器系统（MSS）地理配准是工程中的一项艰巨任务，应以尽可能最准确的方式进行处理。最简单，最直接的方法是依靠全球导航卫星系统（GNSS）和惯性测量单元（IMU）数据。但是，在室内环境或拥挤的市中心地区，此类数据不准确，无法完全依赖。因此，需要适当的滤波算法来补偿可能的误差并提高结果的准确性。有时，还可以通过使用可以直接影响输出的其他补充信息来提高过滤技术的功能。这样的信息可以是例如MSS运行所处环境的几何特征。当前论文通过双状态迭代扩展卡尔曼滤波器（DSIEKF）来处理MSS地理配准，该模型基于迭代扩展卡尔曼滤波器（IEKF）与隐式测量方程技术和非线性几何约束的有效组合。这样的算法的最终结果不仅从准确性的角度而且在计算时间上都显示出令人满意的效果。

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