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

Abstract

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 highrate 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 humanmade scenarios. To benefit the research community, we opensource our code at https://github.com/APRIL-ZJU/ lidar_IMU_calib

GIL: a tightly coupled GNSS PPP/INS/LiDAR method for precise vehicle navigation

“…Time synchronization on the hardware level unifies timestamps of all collected data to GPS time. For spatial synchronization, pre-calibration was performed to identify the extrinsic parameters between LiDAR and IMU (Lv et al, 2020), and the lever arms between IMUs and the GNSS antenna were measured accurately.…”

3D LiDAR/IMU Calibration Based on Continuous-Time Trajectory Estimation in Structured Environments

“…Modeling discrete poses as a continuous-time trajectory is an ingenious idea to solve the above issues, and the exact reference poses of each laser point can be queried with scanning timestamps. This method has been widely employed in many autocalibration approaches, such as those in [19]- [23]. Furgale and Rehder et al presented a series of pioneering works related to temporal-spatial calibration of multiple sensors.…”

“…However, the prerequisite of a sufficiently accurate visual/inertial trajectory makes it unable to calibrate LiDAR/IMU directly. [23] proposed a continuous-time batch optimization-based LiDAR/IMU calibration method, which similarly formulated the trajectory of LiDAR as the B-spline to address the discrete scanning issue. However, the temporal-offset is not considered, and usage of the Bspline function results in more parameters to be estimated.…”

“…In the third test, we calibrated the extrinsic parameters T b l1 and T b l2 between two LiDARs and an IMU. The state-of-art calibration method Li-Calib [23] was selected for comparison. The results are listed in Tab.2.…”

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

“…We choose the LiDAR frame as the reference to calibrate the extrinsic parameters (relative poses) between sensors. We use the toolbox [52] to calibrate the extrinsic parameters between IMU and LiDAR, and Kalibr toolbox [53] to calibrate the extrinsic parameters between cameras and IMU, as well as Autoware software [54] to calibrate extrinsic parameters between LiDAR and cameras.…”