# Gaussian-LIC **Repository Path**: xiaoxinslam/Gaussian-LIC ## Basic Information - **Project Name**: Gaussian-LIC - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-07-10 - **Last Updated**: 2026-07-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Gaussian-LIC: Real-Time Photo-Realistic SLAM with Gaussian Splatting and LiDAR-Inertial-Camera Fusion

ICRA 2025

Paper PDF Project Page

Gaussian-LIC is a photo-realistic LiDAR-Inertial-Camera Gaussian Splatting SLAM system, which simultaneously performs robust, accurate pose estimation and constructs a photo-realistic 3D Gaussian map in real time.

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### 📢 News - [2026-02-21] Gaussian-LIC2 is released! 🚀 (stay tuned for updates) - [2025-07-08] Gaussian-LIC2 is unveiled! 🎉 [[`Paper`](https://arxiv.org/pdf/2507.04004)] [[`Page`](https://xingxingzuo.github.io/gaussian_lic2/)] [[`YouTube`](https://www.youtube.com/watch?v=SkPnpuCfh88)] [[`bilibili`](https://www.bilibili.com/video/BV1fJ3kzfEYv/?spm_id_from=333.337.search-card.all.click&vd_source=99ac6409fc9373f3960feff31c28a189)] - [2025-07-07] The enhanced version of the Gaussian-LIC code is released! - [2025-01-28] Gaussian-LIC is accepted to ICRA 2025! 🎉 - [2024-09-26] The second version of the paper is available on arXiv. - [2024-04-10] The first version of the paper is available on arXiv. ### 💌 Contact Questions? Please don't hesitate to reach out to Xiaolei Lang (Jerry) at jerry_locker@zju.edu.cn. ## Install We test on ubuntu 20.04 with an NVIDIA RTX 3090 / 4090. 1. Exit Conda environment. 2. Prepare third-party libraries according to [Coco-LIC](https://github.com/APRIL-ZJU/Coco-LIC). 3. Install [CUDA 11.7](https://developer.nvidia.com/cuda-11-7-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=runfile_local) with [cuDNN v8.9.7](https://developer.nvidia.com/rdp/cudnn-archive). 4. Build [OpenCV 4.7.0](https://github.com/opencv/opencv/archive/refs/tags/4.7.0.tar.gz).（must be built with [opencv_contrib 4.7.0](https://github.com/opencv/opencv_contrib/archive/refs/tags/4.7.0.tar.gz) and CUDA, no installation required） ```shell mkdir -p ~/Software/opencv cd ~/Software/opencv wget https://github.com/opencv/opencv/archive/refs/tags/4.7.0.tar.gz && tar -zxvf 4.7.0.tar.gz && rm -rf 4.7.0.tar.gz wget https://github.com/opencv/opencv_contrib/archive/refs/tags/4.7.0.tar.gz && tar -zxvf 4.7.0.tar.gz && rm -rf 4.7.0.tar.gz cd ~/Software/opencv/opencv-4.7.0 mkdir build && cd build cmake -DCMAKE_BUILD_TYPE=RELEASE -DWITH_CUDA=ON -DWITH_CUDNN=ON -DOPENCV_DNN_CUDA=ON -DWITH_NVCUVID=ON -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-11.7 -DOPENCV_EXTRA_MODULES_PATH="../../opencv_contrib-4.7.0/modules" -DBUILD_TIFF=ON -DBUILD_ZLIB=ON -DBUILD_JASPER=ON -DBUILD_CCALIB=ON -DBUILD_JPEG=ON -DWITH_FFMPEG=ON .. make -j$(nproc) ``` 5. Prepare [LibTorch](https://pytorch.org/get-started/locally/).（no compilation or installation required） ```shell cd ~/Software wget https://download.pytorch.org/libtorch/cu117/libtorch-cxx11-abi-shared-with-deps-2.0.1%2Bcu117.zip unzip libtorch-cxx11-abi-shared-with-deps-2.0.1+cu117.zip && rm -rf libtorch-cxx11-abi-shared-with-deps-2.0.1+cu117.zip ``` 6. Prepare [TensorRT](https://developer.nvidia.com/tensorrt/download).（no compilation or installation required） ```shell cd ~/Software wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-11.8.tar.gz tar -zxvf TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-11.8.tar.gz && rm -rf TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-11.8.tar.gz ``` 7. Install Coco-LIC. ```shell mkdir -p ~/catkin_coco/src cd ~/catkin_coco/src git clone https://github.com/Livox-SDK/livox_ros_driver.git cd ~/catkin_coco && catkin_make cd ~/catkin_coco/src git clone https://github.com/APRIL-ZJU/Coco-LIC.git cd ~/catkin_coco && catkin_make ``` 8. Install Gaussian-LIC. ```shell mkdir -p ~/catkin_gaussian/src cd ~/catkin_gaussian/src git clone https://github.com/APRIL-ZJU/Gaussian-LIC.git cd ~/catkin_gaussian && catkin_make ``` 9. TensorRT Deployment. download and save [Large_300.pth](https://drive.google.com/file/d/11dujPviL4pKLEXytXK0mEmPBNQDqgEak/view?pli=1) to `~/catkin_gaussian/src/Gaussian-LIC/ckpt`. ```shell cd ~/catkin_gaussian/src/Gaussian-LIC/ckpt chmod +x setup_spnet.sh ./setup_spnet.sh chmod +x export_onnx.sh ./export_onnx.sh chmod +x build_trt.sh ./build_trt.sh ``` ## Run Quick start on the sequence CBD_Building_01 in the FAST-LIVO2 dataset. - Download [FAST-LIVO Dataset](https://connecthkuhk-my.sharepoint.com/personal/zhengcr_connect_hku_hk/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fzhengcr%5Fconnect%5Fhku%5Fhk%2FDocuments%2FFAST%2DLIVO%2DDatasets&ga=1) or [FAST-LIVO2 Dataset](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/zhengcr_connect_hku_hk/ErdFNQtjMxZOorYKDTtK4ugBkogXfq1OfDm90GECouuIQA?e=KngY9Z) or [R3LIVE Dataset](https://github.com/ziv-lin/r3live_dataset) or [MCD Dataset](https://mcdviral.github.io/) or [M2DGR Dataset](https://github.com/SJTU-ViSYS/M2DGR). + Modify `bag_path` in the `config/ct_odometry_fastlivo2.yaml` file of Coco-LIC. + Launch Gaussian-LIC. ```shell cd ~/catkin_gaussian source devel/setup.bash roslaunch gaussian_lic fastlivo2.launch // The terminal will print "😋 Gaussian-LIC Ready!". ``` + Launch Coco-LIC. Note：For real-time use and runtime analysis, please turn off the rviz in Coco-LIC by commenting the sentence `` in `odometry.launch`. ```shell cd ~/catkin_coco source devel/setup.bash roslaunch cocolic odometry.launch config_path:=config/ct_odometry_fastlivo2.yaml ``` + The mapping and rendering results will be saved in `~/catkin_gaussian/src/Gaussian-LIC/result`. ## Checklist - [ ] Support fast post-optimization - [ ] Release the optimized Coco-LIC - [ ] Provide the dockerfile - [ ] Release the meshing tools - [ ] Release our Gaussian-LIC2 dataset ## Citation If you find our work helpful, please consider citing 🌟: ```bibtex @inproceedings{lang2025gaussian, title={Gaussian-LIC: Real-time photo-realistic SLAM with Gaussian splatting and LiDAR-inertial-camera fusion}, author={Lang, Xiaolei and Li, Laijian and Wu, Chenming and Zhao, Chen and Liu, Lina and Liu, Yong and Lv, Jiajun and Zuo, Xingxing}, booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)}, pages={8500--8507}, year={2025}, organization={IEEE} } ``` ```bibtex @article{lang2025gaussian2, title={Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM}, author={Lang, Xiaolei and Lv, Jiajun and Tang, Kai and Li, Laijian and Huang, Jianxin and Liu, Lina and Liu, Yong and Zuo, Xingxing}, journal={arXiv}, year={2025} } ``` ## Acknowledgement Thanks for [3DGS](https://github.com/graphdeco-inria/gaussian-splatting), [Taming-3DGS](https://github.com/humansensinglab/taming-3dgs), [StopThePop](https://github.com/r4dl/StopThePop), [Photo-SLAM](https://github.com/HuajianUP/Photo-SLAM) and [SPNet](https://github.com/Wang-xjtu/SPNet). ## LICENSE The code is released under the [GNU General Public License v3 (GPL-3)](https://www.gnu.org/licenses/gpl-3.0.txt).