Learning-Based Motion-Aware Dead Reckoning Using IMU and Wheel Encoders

Dead Reckoning (DR) algorithms using IMU and wheel encoders can continuously provide positioning information in scenarios where GNSS signals are lost, providing strong autonomy and playing a critical role in vehicle autonomous navigation. However, a limitation of this method is that errors accumulate over time, resulting in decreasing positioning accuracy and even significant drift during long-term use. This paper proposes a learning-based dead reckoning algorithm that leverages an encoder-decoder neural network integrating a multi-modal attention module and a coarse-to-fine fusion strategy. This approach effectively extracts and fuses motion characteristics from IMU and wheel encoders data, thereby accurately estimating the 3D vehicle’s instant velocity in the world frame and quantifying its uncertainty. The predicted velocity is then used as the pseudo-measurement in an invariant extended Kalman filter (IEKF), where the uncertainty is directly mapped into a dynamical measurement noise covariance matrix that adjusts the filter update. Real-world experiments demonstrate that our method outperforms existing techniques, reducing average positioning error by approximately 76% compared to the traditional INS+ODO baseline and guaranteeing robust long-term localization in dynamic environments.