Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation
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Abstract (arXiv)
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 urlhttps://github.com/APRIL-ZJU/lidarIMUcalib
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.
6 papers.
- 2021M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robotscited by 23
- 2023AirIMU: Learning Uncertainty Propagation for Inertial Odometrycited by 6
- 2022Evaluation and comparison of eight popular Lidar and Visual SLAM algorithmscited by 2
- 2025Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learnedcited by 1
- 2025Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localizationcited by 0
- 2026Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systemscited by 0
- 2026Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems24 Sep 2026
- 2025Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learned19 Jun 2025
- 2025Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization29 Mar 2025
- 2023AirIMU: Learning Uncertainty Propagation for Inertial Odometry7 Oct 2023
- 2022Evaluation and comparison of eight popular Lidar and Visual SLAM algorithms3 Aug 2022
- 2021M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots19 Dec 2021