base on GLIM: versatile and extensible point cloud-based 3D localization and mapping framework  ## 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) - 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](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_ros1](https://hub.docker.com/repository/docker/koide3/glim_ros1/tags), [koide3/glim_ros2](https://hub.docker.com/repository/docker/koide3/glim_ros2/tags), [ROS1 on Jetpack 5.1.4](https://hub.docker.com/r/junekyoopark/arm64v8_glim_ros1_cuda12.2) (made by [junekyoopark](https://github.com/junekyoopark)) **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 / 12.5 / 12.6, 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_ros1/actions/workflows/build.yml) [](https://github.com/koide3/glim_ros2/actions/workflows/build.yml) [](https://github.com/koide3/glim_ext/actions/workflows/build.yml) ## 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 | |---|---| |[<img width="480" src="https://github.com/user-attachments/assets/95e153cd-1538-4ca6-8dd0-691e920dccd9">](https://www.youtube.com/watch?v=_fwK4awbW18)|[<img width="480" src="https://github.com/user-attachments/assets/6b337369-a32c-4b07-b0e0-b63f6747cdab">](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 ```[email protected]```. 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/), [email protected]<br> National Institute of Advanced Industrial Science and Technology (AIST), Japan ", Assign "at most 3 tags" to the expected json: {"id":"12997","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"