Inertial measurement units (IMUs) are successfully utilized to compensate localization errors in sensor fused inertial navigation systems. An IMU generally produces high-frequency signals ranging from 100 to 1000 Hz, and preintegration methods are applied to effectively process these high-frequency signals for inertial navigation systems. The main problem with an existing preintegration method is that the inertial propagation models in the method are only generated at the IMU's coordinate system. Hence, the models have to be converted to the coordinate system of the other sensor in order to apply its constraint. So, the iterative optimization framework using the conventional method takes large amount of time. In addition, since a general rigid body transformation cannot transfer a velocity propagation model to the other coordinate system, the concept of relative motion analysis needs to be considered. To solve the problems above, in this paper, we propose a novel relative preintegration (RP) method that can generate inertial propagation models at any sensor's coordinate system in a rigid body. This permits accurate and fast IMU processing in sensor fused inertial navigation systems. We applied new nonlinear optimization frameworks to solve initialization and extrinsic calibration problems for the IMU-IMU, IMU-Camera, and IMU-LiDAR pair based on the proposed RP method in an on-line manner, and the superior results of the mentioned processes are presented as well.

中文翻译：

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流形相对预积分惯性导航系统在线初始化与外参标定


惯性测量单元 (IMU) 已成功用于补偿传感器融合惯性导航系统中的定位误差。 IMU通常会产生100到1000 Hz范围内的高频信号，并且应用预积分方法来有效地处理这些高频信号以供惯性导航系统使用。现有预积分方法的主要问题是该方法中的惯性传播模型仅在IMU坐标系下生成。因此，必须将模型转换为其他传感器的坐标系才能应用其约束。因此，使用传统方法的迭代优化框架需要花费大量时间。此外，由于一般的刚体变换无法将速度传播模型转移到其他坐标系，因此需要考虑相对运动分析的概念。为了解决上述问题，在本文中，我们提出了一种新颖的相对预积分（RP）方法，可以在刚体中的任何传感器坐标系上生成惯性传播模型。这使得传感器融合惯性导航系统能够进行准确、快速的 IMU 处理。我们应用新的非线性优化框架，基于所提出的 RP 方法以在线方式解决 IMU-IMU、IMU-Camera 和 IMU-LiDAR 对的初始化和外部校准问题，上述过程的优越结果为也提出了。