IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving

For autonomous driving, it is important to obtain precise and high-frequency localization information. This paper proposes a novel method in which the Inertial Measurement Unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of an unmanned ground vehicle. The IMU is fused with the wheel encoder to obtain the motion prior, and it is involved in three levels of the lidar odometry: Firstly, we use the IMU information to rectify the intra-frame distortion of the lidar scan, which is caused by the vehicle's own movement; secondly, the IMU provides a better initial guess for the lidar odometry; and thirdly, the IMU is fused with the lidar odometry in an Extended Kalman filter framework. In addition, an efficient method for hand-eye calibration between the IMU and the lidar is proposed. To evaluate the performance of our method, extensive experiments are performed and our system can output stable, accurate, and high-frequency localization results in diverse environment without any prior information.