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Gaussian-LIC

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[ICRA 2025 & IJRR 2026] Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM (in Real Time)

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- - config.py
  - test.py
  - test_utils.py
  - train.py
- build_trt.sh
- export_onnx.sh
- export_onnx_480_640.py
- export_onnx_512_640.py
- setup_spnet.sh
- fastlivo.yaml
- fastlivo2.yaml
- m2dgr.yaml
- mcd.yaml
- r3live.yaml
- f2_compressed.gif
- r0_compressed.gif
- r1_compressed.gif
- fastlivo.launch
- fastlivo2.launch
- m2dgr.launch
- mcd.launch
- r3live.launch
- - ssim.cu
  - ssim.h
- - lpips_alex.pt
  - save_alex.py
  - save_vgg.py
- - rasterize_points.cu
  - rasterize_points.h
  - rasterizer.cpp
  - rasterizer.h
  - renderer.cpp
  - renderer.h
- - simple_knn.cu
  - simple_knn.h
  - spatial.cu
  - spatial.h
- camera.h
- depth_completer.cpp
- depth_completer.h
- eigen_utils.h
- gaussian.cpp
- gaussian.h
- general_utils.h
- loss_utils.h
- mapping.cpp
- mapping.h
- optim_utils.h
- tensor_utils.h
- tinyply.cpp
- tinyply.h
- yaml_utils.h
.gitignore
CMakeLists.txt
package.xml
README.md

# Installation Guide

1. Get the code

git clone https://github.com/APRIL-ZJU/Gaussian-LIC

Downloads the entire project code from GitHub to your computer.

cd Gaussian-LIC

Moves into the project folder you just downloaded.

2. CMake

Medium Recommended

Prerequisites

Git Needed to download the project code from GitHub.
CMake The tool used to generate build configuration.
C/C++ 컴파일러 Windows needs Visual Studio (Community edition, free), macOS needs Xcode Command Line Tools, Linux needs the gcc/g++ package.

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 ..

Type this command into your terminal and run it.

✅ Check that an executable was created inside the build folder, then run it directly (e.g. ./build/app_name).

Pulled directly from this repo's README.

// repository documentation

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<p align="center">
  <h1 align="center">Gaussian-LIC2: LiDAR-Inertial-Camera<br> Gaussian Splatting SLAM</h1>
  <p align="center">
    ICRA 2025 & IJRR 2026
  </p>
  <p align="center">
    <a href="https://arxiv.org/pdf/2404.06926">
      <img src='https://img.shields.io/badge/Gaussian--LIC-Paper-red?style=flat&logo=arXiv&logoColor=red' alt='Gaussian-LIC Paper PDF'>
    </a>
    <a href="https://arxiv.org/pdf/2507.04004" style='padding-left: 0.5rem;'>
      <img src='https://img.shields.io/badge/Gaussian--LIC2-Paper-red?style=flat&logo=arXiv&logoColor=red' alt='Gaussian-LIC2 Paper PDF'>
    </a>
    <a href='https://xingxingzuo.github.io/gaussian_lic2/' style='padding-left: 0.5rem;'>
      <img src='https://img.shields.io/badge/Project-Page-blue?style=flat&logo=Google%20chrome&logoColor=blue' alt='Project Page'>
    </a>
  </p>
</p>

Gaussian-LIC2 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.

<p align="center">
    <img src="figure/r1_compressed.gif" alt="Logo" width="32%">
    <img src="figure/r0_compressed.gif" alt="Logo" width="32%">
    <img src="figure/f2_compressed.gif" alt="Logo" width="32%">
</p>

### 📢 News

- [2026-07-25] Gaussian-LIC2 is accepted by the International Journal of Robotics Research (IJRR)! 🎉
- [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 `<node pkg="rviz" type="rviz" name="rviz_odom" output="log" required = "true" args="-d $(find cocolic)/config/coco.rviz" />`  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{lang2026gaussian2,
  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={The International Journal of Robotics Research},
  year={2026}
}
```

## 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).