# glim
**Repository Path**: ruinco/glim
## Basic Information
- **Project Name**: glim
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-03-06
- **Last Updated**: 2026-03-06
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
## Introduction
**GLIM** is a versatile and extensible range-based 3D mapping framework.
- ***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.
- ***Easy-to-use:*** GLIM offers an interactive map correction interface that enables the user to manually correct mapping failures and easily refine mapping results.
- ***Versatility:*** As we eliminated sensor-specific processes, GLIM can be applied to any kind of range sensors including:
- Spinning-type LiDAR (e.g., Velodyne HDL32e and Ouster OS1-32)
- Non-repetitive scan LiDAR (e.g., Livox Avia and MID360)
- Solid-state LiDAR (e.g., Intel Realsense L515)
- RGB-D camera (e.g., Microsoft Azure Kinect)
- ***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).
**Documentation: [https://koide3.github.io/glim/](https://koide3.github.io/glim/)**
**Docker hub:** [koide3/glim_ros2](https://hub.docker.com/repository/docker/koide3/glim_ros2/tags)
**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)
Tested on Ubuntu 22.04 / 24.04 with CUDA 12.2 / 12.6 / 13.1, and NVIDIA Jetson Orin (Jetpack 6.1).
If 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.
[](https://github.com/koide3/glim/actions/workflows/build.yml)
[](https://github.com/koide3/glim_ros2/actions/workflows/build.yml)
[](https://github.com/koide3/glim_ext/actions/workflows/build.yml)
## Updates
- 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.
- 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**.
## Dependencies
### Mandatory
- [Eigen](https://eigen.tuxfamily.org/index.php)
- [nanoflann](https://github.com/jlblancoc/nanoflann)
- [GTSAM](https://github.com/borglab/gtsam)
- [gtsam_points](https://github.com/koide3/gtsam_points)
### Optional
- [CUDA](https://developer.nvidia.com/cuda-toolkit)
- [OpenCV](https://opencv.org/)
- [OpenMP](https://www.openmp.org/)
- [ROS/ROS2](https://www.ros.org/)
- [Iridescence](https://github.com/koide3/iridescence)
## Gallery
See more at [Video Gallery](https://github.com/koide3/glim/wiki/Video-Gallery).
| Mapping with various range sensors | Outdoor driving test with Livox MID360 |
|---|---|
|[
](https://www.youtube.com/watch?v=_fwK4awbW18)|[
](https://www.youtube.com/watch?v=CIfRqeV0irE)|
| Manual loop closing | Merging multiple mapping sessions |
|---|---|
|||
| Object segmentation and removal | |
|---|---|
|| |
## Estimation modules
GLIM 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.
## Thirdparty works using GLIM
If you are willing to add your work here, feel free to let me know in [this thread](https://github.com/koide3/glim/issues/19) :)
- [kamibukuro5656/MapCleaner_Unofficial](https://github.com/kamibukuro5656/MapCleaner_Unofficial)
## License
This package is released under the MIT license. For commercial support, please contact ```k.koide@aist.go.jp```.
If 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.
## Related work
Koide 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)
The GLIM framework involves ideas expanded from the following papers:
- (LiDAR-IMU odometry and mapping) "Globally Consistent and Tightly Coupled 3D LiDAR Inertial Mapping", ICRA2022 [[DOI]](https://doi.org/10.1109/ICRA46639.2022.9812385)
- (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)
- (GPU-accelerated scan matching) "Voxelized GICP for Fast and Accurate 3D Point Cloud Registration", ICRA2021, [[DOI]](https://doi.org/10.1109/ICRA48506.2021.9560835)
## Contact
[Kenji Koide](https://staff.aist.go.jp/k.koide/), k.koide@aist.go.jp
National Institute of Advanced Industrial Science and Technology (AIST), Japan