Quaternion-Based Iterative Extended Kalman Filter for Sensor Fusion of Vision Sensor and IMU in 6-DOF Displacement Monitoring

Abstract

As civil structures are exposed to various external loads, their periodic evaluation is paramount to ensure their safety. By estimating the 6-degree-of-freedom (DOF) displacement of structures, structural behavior can be monitored directly. Therefore, this study aims to develop a translational and rotational displacement estimation method by fusing a vision sensor and inertial measurement unit (IMU) using a quaternion-based iterative extended Kalman filter (QIEKF). The QIEKF algorithm was applied to reduce the nonlinear influence on the measurement model. The 6-DOF displacement is predicted using the integral of the gyroscope output and updated via a combination of an accelerometer and a magnetometer through a vector matching process in the Kalman filter framework. Subsequently, the 6-DOF displacement estimation result is updated through a vision sensor using a 2-D planar marker and homography transformation in the Kalman filter framework. The performance of the proposed sensor fusion method was verified with experiments using a motorized motion stage, and the results show that the displacements can be estimated with high accuracy regardless of measurement noise and slowly varying signal drift.