Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation

Sensor calibration is the fundamental block for a multi-sensor fusion system. This paper presents an accurate and repeatable LiDAR-IMU calibration method (termed LI-Calib), to calibrate the 6-DOF extrinsic transformation between the 3D LiDAR and the Inertial Measurement Unit (IMU). Regarding the high data capture rate for LiDAR and IMU sensors, LI-Calib adopts a continuous-time trajectory formulation based on B-Spline, which is more suitable for fusing high-rate or asynchronous measurements than discrete-time based approaches. Additionally, LI-Calib decomposes the space into cells and identifies the planar segments for data association, which renders the calibration problem well-constrained in usual scenarios without any artificial targets. We validate the proposed calibration approach on both simulated and real-world experiments. The results demonstrate the high accuracy and good repeatability of the proposed method in common human-made scenarios. To benefit the research community, we open-source our code at https://github.com/APRIL-ZJU/lidar_IMU_calib.

I. INTRODUCTION

Multi-sensor fusion has been an essential developing trend in the robotics field. In particular, the LiDAR sensor is widely employed due to its high accuracy, robustness, and reliability, such as for localization, semantic mapping, object tracking, detection [1]–[5]. Despite these advantages of LiDAR sensors, the downside is from the fact that the LiDAR samples a succession of 3D-points at different times, thus the motion of the sensor carrier introduces distortion like the rolling-shutter effect. To address this issue, the Inertial Measurement Unit (IMU), widely used for the ego-motion estimation at a high frequency, can be utilized as a complementary sensor to correct the distortion. In general, a LiDAR-IMU system benefits from the component sensors and becomes feasible for reliable perception in various scenarios.