{"repo":"koide3/glim","free":true,"listed":false,"github":"https://github.com/koide3/glim","clone":"git clone https://github.com/koide3/glim.git","description":"GLIM: versatile and extensible point cloud-based 3D localization and mapping framework","language":"C++","stars":1743,"topics":["3d","gpu","imu","lidar","localization","mapping","rgb-d","ros","ros2","slam"],"license":"MIT","category":"slam_framework","readme_excerpt":"![GLIM](docs/assets/logo2.png \"GLIM Logo\")\n\n## Introduction\n\n**GLIM** is a versatile and extensible range-based 3D mapping framework.\n\n- ***Accuracy:*** GLIM is based on direct multi-scan registration error minimization on factor graphs that enables to accurately retain the consistency of mapping results. GPU acceleration is supported to maximize the mapping speed and quality.\n- ***Easy-to-use:*** GLIM offers an interactive map correction interface that enables the user to manually correct mapping failures and easily refine mapping results.\n- ***Versatility:*** As we eliminated sensor-specific processes, GLIM can be applied to any kind of range sensors including:\n - Spinning-type LiDAR (e.g., Velodyne HDL32e and Ouster OS1-32)\n - Non-repetitive scan LiDAR (e.g., Livox Avia and MID360)\n - Solid-state LiDAR (e.g., Intel Realsense L515)\n - RGB-D camera (e.g., Microsoft Azure Kinect)\n- ***Extensibility:*** GLIM provides the global callback slot mechanism that allows to access the internal states of the mapping process and insert additional constraints to the factor graph. We also release [glim_ext](https://github.com/koide3/glim_ext) that offers example implementations of several extension functions (e.g., explicit loop detection, LiDAR-Visual-Inertial odometry estimation).\n\n**Documentation: [https://koide3.github.io/glim/](https://koide3.github.io/glim/)**\n**Docker hub:** [koide3/glim_ros2](https://hub.docker.com/repository/docker/koide3/glim_ros2/tags)\n**Related packages:** [gtsam_points](https://github.com/koide3/gtsam_points), [glim](https://github.com/koide3/glim), ~~[glim_ros1](https://github.com/koide3/glim_ros1),~~ [glim_ros2](https://github.com/koide3/glim_ros2), [glim_ext](https://github.com/koide3/glim_ext)\n\nTested on Ubuntu 22.04 / 24.04 with CUDA 12.2 / 12.6 / 13.1, and NVIDIA Jetson Orin (Jetpack 6.1).\n\nIf you find this package useful for your project, please consider leaving a comment [here](https://github.com/koide3/glim/issues/19). It would help the author receive recognition in his organization and keep working on this project.\n\n[](https://github.com/koide3/glim/actions/workflows/build.yml)\n[](https://github.com/koide3/glim_ros2/actions/workflows/build.yml)\n[](https://github.com/koide3/glim_ext/actions/workflows/build.yml)\n\n## Updates\n\n- 2026/07/12 : v1.2.2 released. Fix errors on GTSAM 4.3a1.\n- 2026/02/15 : v1.2.1 released. Backface culling support.\n- 2026/01/24 : v1.2.0 released. Added Support for both **GTSAM 4.2a9** and **GTSAM 4.3a0**, and **CUDA 13.1**. Added intensity visualization support.\n- 2025/06/15 : The base GTSAM version has been changed. Make sure you have rebuilt and installed **GTSAM 4.3a0** and **gtsam_points 1.2.0**.\n\n## Dependencies\n### Mandatory\n- [Eigen](https://eigen.tuxfamily.org/index.php)\n- [nanoflann](https://github.com/jlblancoc/nanoflann)\n- [GTSAM](https://github.com/borglab/gtsam)\n- [gtsam_points](https://github.com/koide3/gtsam_points)\n\n### Optional\n- [CUDA](https://developer.nvidia.com/cuda-toolkit)\n- [OpenCV](https://opencv.org/)\n- [OpenMP](https://www.openmp.org/)\n- [ROS/ROS2](https://www.ros.org/)\n- [Iridescence](https://github.com/koide3/iridescence)\n\n## Gallery\n\nSee more at [Video Gallery](https://github.com/koide3/glim/wiki/Video-Gallery).\n\n| Mapping with various range sensors | Outdoor driving test with Livox MID360 |\n|---|---|\n|[![](\"https://github.com/user-attachments/assets/95e153cd-1538-4ca6-8dd0-691e920dccd9\")
](https://www.youtube.com/watch?v=_fwK4awbW18)|[![](\"https://github.com/user-attachments/assets/6b337369-a32c-4b07-b0e0-b63f6747cdab\")
](https://www.youtube.com/watch?v=CIfRqeV0irE)|\n\n| Manual loop closing | Merging multiple mapping sessions |\n|---|---|\n|||\n\n| Object segmentation and removal | |\n|---|---|\n|| |\n\n## Estimation modules\n\nGLIM provides several estimation modules to cover use scenarios, from robust and accurate mapping with a GPU to lightweight real-time mapping with a low-specification PC like Raspberry Pi.\n\n\n\n## Thirdparty works using GLIM\n\nIf you are willing to add your work here, feel free to let me know in [this thread](https://github.com/koide3/glim/issues/19) :)\n\n- [kamibukuro5656/MapCleaner_Unofficial](https://github.com/kamibukuro5656/MapCleaner_Unofficial)\n\n## License\n\nThis package is released under the MIT license. For commercial support, please contact ```k.koide@aist.go.jp```.\n\nIf you find this package useful for your project, please consider leaving a comment [here](https://github.com/koide3/glim/issues/19). It would help the author receive recognition in his organization and keep working on this project. Please also cite the following paper if you use this package in your academic work.\n\n## Related work\n\nKoide et al., \"GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors\", Robotics and Autonomous Systems, 2024, [[DOI]](https://doi.org/10.1016/j.robot.2024.104750) [[Arxiv]](https://arxiv.org/abs/2407.10344)\n\nThe GLIM framework involves ideas expanded from the following papers:\n- (LiDAR-IMU odometry and mapping) \"Globally Consistent and Tightly Coupled 3D LiDAR Inertial Mapping\", ICRA2022 [[DOI]](https://doi.org/10.1109/ICRA46639.2022.9812385)\n- (Global registration error minimization) \"Globally Consistent 3D LiDAR Mapping with GPU-accelerated GICP Matching Cost Factors\", IEEE RA-L, 2021, [[DOI]](https://doi.org/10.1109/LRA.2021.3113043)\n- (GPU-accelerated scan matching) \"Voxelized GICP for Fast and Accurate 3D Point Cloud Registration\", ICRA2021, [[DOI]](https://doi.org/10.1109/ICRA48506.2021.9560835)\n\n## Contact\n[Kenji Koide](https://staff.aist.go.jp/k.koide/), k.koide@aist.go.jp
\nNational Institute of Advanced Industrial Science and Technology (AIST), 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