├── figure.png ├── LICENSE └── README.md /figure.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Taeyoung96/Awesome-LiDAR-IMU-calibration/HEAD/figure.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 TaeYoung Kim 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-LiDAR-IMU-calibration [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) 2 | 3 | :sunglasses: A current list of LiDAR-IMU calibration method 4 | 5 | ## Introduction 6 | 7 | LiDAR and IMU are among the widely used sensors in the field of self-driving cars and robotics. To fuse both sensors and use them for algorithms (such as LiDAR-inertial SLAM), it is essential to obtain the exact extrinsic parameter. 8 | 9 | Inspired by [Deephome/Awesome-LiDAR-Camera-Calibration](https://github.com/Deephome/Awesome-LiDAR-Camera-Calibration), this repository summarizes the LiDAR-IMU calibration methods currently being studied in research fields and related toolboxes. 10 | 11 |

12 | The figure above is one of the figures in the paper "Target-free Extrinsic Calibration of a 3D-Lidar and an IMU". 13 | 14 | ## Related papers 15 | Target means calibration target. 16 | **"S"** means spatial information (Transformation matrix) and **"T"** means temporal information (time offset). 17 | 18 | |Paper|Published|Target|Key words|Code| 19 | | --- | --- | --- | --- | --- | 20 | |3D Lidar-IMU Calibration Based on Upsampled Preintegrated Measurements for Motion Distortion Correction|[ICRA 2018](https://ieeexplore.ieee.org/document/8460179)|S+T|IMU Preintegration, Plane association|-| 21 | |Error modeling and extrinsic–intrinsic calibration for LiDAR-IMU system based on cone-cylinder features|[RAS 2019](https://www.sciencedirect.com/science/article/pii/S092188901730636X)|S| Cone-cylinder features, IMU intrinsic parameter, EKF based |-| 22 | |Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation|[IROS 2020](https://ieeexplore.ieee.org/abstract/document/9341405)|S+T|Continous time trajectory, Surfel map|[LI-Calib](https://github.com/APRIL-ZJU/lidar_IMU_calib)| 23 | |A Novel Multifeature Based On-Site Calibration Method for LiDAR-IMU System|[TIE 2020](https://ieeexplore.ieee.org/abstract/document/8924904)|S|Multi-type geometric features, Cone-cylinder features(RAS 2019) extended version |-| 24 | |Motion-based Calibration between Multiple LiDARs and INS with Rigid Body Constraint on Vehicle Platform|[IV 2020](https://ieeexplore.ieee.org/abstract/document/9304532)|S|Graph structure-based optimization, Multiple LiDAR |-| 25 | |Efficient Multi-sensor Aided Inertial Navigation with Online Calibration|[ICRA 2021](https://ieeexplore.ieee.org/abstract/document/9561254)|S+T|MSCKF based, Multi-sensor INS, |-| 26 | |Target-free Extrinsic Calibration of a 3D-Lidar and an IMU|[MFI 2021](https://ieeexplore.ieee.org/abstract/document/9591180)|S|EKF based|[imu_lidar_calibration](https://github.com/unmannedlab/imu_lidar_calibration)| 27 | |3D LiDAR/IMU Calibration Based on Continuous-Time Trajectory Estimation in Structured Environments|[IEEE Access 2021](https://ieeexplore.ieee.org/abstract/document/9543701)|S|Continuous-Time Trajectory, Gaussian process(GP) regression|-| 28 | |Observability-Aware Intrinsic and Extrinsic Calibration of LiDAR-IMU Systems|[TRO 2022](https://ieeexplore.ieee.org/abstract/document/9787062)|S+T|LI-Calib(IROS 2020) extension version|[OA-LICalib](https://github.com/APRIL-ZJU/OA-LICalib)| 29 | |Robust Real-time LiDAR-inertial Initialization|[IROS 2022](https://arxiv.org/abs/2202.11006)|S+T|IESKF based, FAST-LIO initialization|[LI-Init](https://github.com/hku-mars/LiDAR_IMU_Init)| 30 | |An Extrinsic Calibration Method of a 3D-LiDAR and a Pose Sensor for Autonomous Driving|[Arxiv 2022](https://arxiv.org/pdf/2209.07694.pdf)|S|LiDAR-INS calibration part of OpenCalib|[LiDAR2INS](https://github.com/OpenCalib/LiDAR2INS)| 31 | |AFLI-Calib: Robust LiDAR-IMU extrinsic self-calibration based on adaptive frame length LiDAR odometry|[ISPRS 2023](https://www.sciencedirect.com/science/article/pii/S092427162300093X)|S|Continuous-time model, Optimization based|[AFLI-Calib](https://github.com/DCSI2022/AFLI_Calib)| 32 | |GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method using Ground Plane Motion Constraints|[RAL 2024](https://ieeexplore.ieee.org/document/10506583)|S+T|LiDAR-IMU calibration for ground robot|[GRIL-Calib](https://github.com/Taeyoung96/GRIL-Calib)| 33 | |L2Calib: SE(3)-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience|[IROS 2025](https://arxiv.org/pdf/2508.06330)|S|Reinforcement learning-based extrinsic calibration method|[learn-to-calibrate](https://github.com/APRIL-ZJU/learn-to-calibrate)| 34 | 35 | 36 | ## Other toolboxes 37 | 38 | |ToolBox|Keywords| 39 | | --- | --- | 40 | |[OpenCalib(SensorsCalibration)](https://github.com/PJLab-ADG/SensorsCalibration)|Calibration Toolbox for Autonomous Driving| 41 | |[chennuo0125-HIT/lidar_imu_calib](https://github.com/chennuo0125-HIT/lidar_imu_calib)|Only calculate extrinsic rotation parameter| 42 | |[ethz-asl/lidar_align](https://github.com/ethz-asl/lidar_align)|Accurate results require highly non-planar motions| 43 | 44 | ## Contacts 45 | 46 | If you have any question, feel free to leave an issue or send an email. :smile: 47 | 48 | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 49 | --------------------------------------------------------------------------------