IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving

Abstract

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.

Deeplio: Deep Lidar Inertial Sensor Fusion for Odometry Estimation

“…A comparison to other methods showed that NICP registration offers better results, also it is more robust against poor initial guesses. To improve the ICP-based pose estimation in (Xue et al, 2019) the authors introduce a loosely coupled Extended Kalman-Filterbased IMU-ICP-fusion, where the IMU measurement are used at different processing stages. According to their experiments, the best odometry estimation result is achieved using IMU and a Lidar Odometry and Mapping (LOAM) method (Zhang and Singh, 2014), which is a feature-based point cloud registration approach based on a novel strategy for edge and surface features extraction.…”

Special Issue on Intelligent Processing on Image and Optical Information

“…For autonomous driving, it is important to obtain precise and high-frequency localization of the vehicle. The inertial measurement unit (IMU), wheel encoder, and lidar odometry are utilized together to estimate the ego-motion of the unmanned ground vehicle in the paper entitled 'IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving' by Hanzhang Xue, Hao Fu, and Bin Dai [13].…”

Large-Scale Outdoor SLAM Based on 2D Lidar

“…The aim of scan matching [14] is to estimate the rigid-body transformation between two laser scans. The Iterative Closest Point (ICP) [2] and Iterative Closest Line [15] are the most popular scan matching algorithms which estimate the relative transform by finding the nearest points between the two laser scans.…”