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 mappint 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)
- Non-repetitive scan LiDAR (e.g., Livox Avia)
- 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 that offers example implementations of several extension functions (e.g., explicit loop detection, LiDAR-Visual-Inertial odometry estimation).
Documentation: https://koide3.github.io/glim/
Docker hub: koide3/glim_ros1, koide3/glim_ros2
Related packges: gtsam_points, glim, glim_ros1, glim_ros2, glim_ext
Tested on Ubuntu 22.04 /24.04 with CUDA 12.2, and NVIDIA Jetson Orin.
This package is released under the MIT license. For commercial support, please contact [email protected].
Kenji Koide, [email protected]
National Institute of Advanced Industrial Science and Technology (AIST), Japan