Learning Yaw Velocity for Inertial-Wheel Odometry on Autonomous Vehicles

Abstract

In this paper, we present an inertial-wheel odometry leveraging the learning-based yaw velocity estimations for autonomous vehicles. A novel attention-based neural network, namely YNet, is employed to estimate yaw velocities by integrating multimodal data from the Inertial Measurement Unit (IMU) and wheel encoders. These learning-based estimations are then integrated into a state-of-the-art invariant extended Kalman filter along with the velocity measurements from wheel encoders. Impressive long-term localization performance is achieved by relying solely on motion measurement sensors, namely IMU and wheel encoders. This characteristic grants it remarkable robustness in diverse and complex environments, surpassing the limitations of vision or lidar based odometry methods. The evaluations on the Kaist datasets and real-world experiments demonstrate that the proposed approach effectively reduces the drift of long-term inertial localization, yielding superior results compared to other state-of-the-art methods.