Visual Stabilization for Vehicles Through Inertial Fusion

초록

We propose a drift-aware estimation framework for roll and pitch using an Extended Kalman Filter (EKF) that fuses high-frequency inertial data from an IMU with drift-compensated observations from a visual odometry (VO) system. The EKF state vector explicitly models angle errors, angular velocities, and gyroscope biases, allowing for the long-term correction of integration drift. In the prediction step, IMU angular velocity is propagated through a kinematic model. In contrast, the update step incorporates roll and pitch estimates from VO to correct accumulated drift and refine bias estimates. The fusion process leverages the complementary properties of the two sensors' high-rate IMU samples for responsiveness and low-drift VO samples for stability. We test our method using real-world driving data collected on various terrains, including smooth surfaces, around corners, and on rough terrain. Quantitative results show that our EKFbased approach achieves improved estimation accuracy compared to either IMU or VO alone (up to 45 % improvement in both RMSE and MAE). The proposed method is low-complexity, robust to motion dynamics, and suitable for real-time implementation in fully autonomous navigation systems that require stable orientation across extended timescales.

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