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

arXiv: https://arxiv.org/abs/2007.14759 · HTML: https://mlsanity.com/paper/2007.14759/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots](https://mlsanity.com/paper/2112.13659/) (2021)
- [AirIMU: Learning Uncertainty Propagation for Inertial Odometry](https://mlsanity.com/paper/2310.04874/) (2023)
- [Evaluation and comparison of eight popular Lidar and Visual SLAM algorithms](https://mlsanity.com/paper/2208.02063/) (2022)
- [Reproducible Evaluation of Camera Auto-Exposure Methods in the Field: Platform, Benchmark and Lessons Learned](https://mlsanity.com/paper/2506.18844/) (2025)
- [Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization](https://mlsanity.com/paper/2503.23199/) (2025)
- [L2Calib: SE(3)-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience](https://mlsanity.com/paper/2508.06330/) (2025)
- [Free-Init: Scan-Free, Motion-Free, and Correspondence-Free Initialization for Doppler LiDAR-Inertial Systems](https://mlsanity.com/paper/2609.29375/) (2026)

## 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