Motion-Acceleration Calibration and Compensation in IMUs without External Equipment for Attitude Estimation Filters

2026-07-28

AI Summary

During motion, inertial measurement units (IMUs) are subject to non-gravitational accelerations—such as centripetal and tangential components—which distort gravity direction estimation, particularly when the IMU is mounted far from the system’s center of rotation. This work proposes a self-calibration method that requires no external equipment and jointly estimates IMU intrinsic parameters (axis misalignment, bias, and scale factors) and extrinsic parameters (the displacement vector from the base frame to the sensor). Crucially, it explicitly models motion-induced acceleration using gyroscope data to compensate gravity observations within the attitude estimation pipeline. To the best of our knowledge, this is the first approach to integrate motion acceleration compensation directly into orientation estimation, enabling seamless compatibility with established filters such as Madgwick and Mahony. Experimental results demonstrate that the proposed framework significantly improves attitude accuracy under high-dynamic conditions and non-central IMU mounting configurations.

Abstract

Attitude estimation based on inertial sensing requires measurements of local angular velocities and local gravity via gyroscopes and accelerometers. However, during the motion of a mobile system the inertial measurement unit (IMU) will be subject to additional accelerations which skews the measurement of local gravity. This effect gets amplified the further away the IMU is from the base of the system. Many attitude estimation filters, such as "Madgwick" or "Mahony", account for this by relying more on gyroscope integration for periods of high angular velocity. However, this approach is prone to accumulate long term error especially around the gravity vector. In this work we utilize the gyroscope measurements to compensate the additional accelerations induced by the motion of the system, i.e., centripetal- and tangential-accelerations. Additionally, we introduce a calibration method that estimates intrinsic IMU parameters such as axes misalignment, bias, scale, as well as the extrinsic base-to-IMU vector without the necessity for additional external equipment. Our evaluation in simulation as well as in the real-world shows that this method improves any attitude filter that relies on the direction of gravity. Furthermore we demonstrate the effectivenes on highly dynamic systems, and systems that are unable to put the IMU at the center of rotation, using our real-world spherical mobile mapping system.

Problem

• IMU
• attitude estimation
• gravity measurement
• motion-induced acceleration
• calibration

Innovation

• IMU calibration
• motion acceleration compensation
• attitude estimation
• gravity vector refinement
• self-contained calibration