RINS-Cali: A Calibration Method for RINS Mounting Errors Based on Screw Theory and Recursive Total Least Square Algorithm

Inertial Measurement Units (IMUs) are widely used in motion estimation and navigation systems, but their accuracy is often degraded by sensor biases and mounting misalignments. Rotation-aided Inertial Navigation Systems (RINS) mitigate IMU biases by introducing controlled sensor rotation; however, uncalibrated mounting errors introduce additional inaccuracies. This paper presents a novel RINS mounting error calibration framework, addressing two angular misalignments and two positional offsets between the IMU and the rotation center. The proposed method first develops a kinematic model based on screw theory to describe the relationship between IMU and motor twists, capturing the effects of misalignment on inertial measurements. A recursive total least squares (RTLS) algorithm is then employed to iteratively estimate and compensate for mounting errors. The approach is validated through both simulation and real-world experiments, demonstrating significant improvements in IMU measurement accuracy and overall system robustness. Experimental results confirm the efficiency of the proposed calibration framework, making it a practical solution for RINS applications in navigation. The code is open-source in https://github.com/ControlSystemLab/RINS-Cali.