On-Line Initialization and Extrinsic Calibration of an Inertial Navigation System With a Relative Preintegration Method on Manifold

Abstract

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. Note to Practitioners —In the proposed method, extrinsic calibration parameters can be estimated during the localization process without any initial manual precalibration procedure. Besides, an IMU is small and inexpensive, so small AGVs in warehouses, or mobile manipulators in factories are good applications for the proposed method, where they require a certain level of localization accuracy. The localization module using the proposed method can be mounted anywhere in a robot without restrictions. The initialization procedure has been executed for a few seconds with arbitrary movement in the field of view of the first frame, then the initial gravity and velocity vector, and extrinsic parameters, which are estimated during the initialization phase, are applied to a general SLAM problem.