# OpenGS-SLAM

**Repository Path**: freecode01n/OpenGS-SLAM

## Basic Information

- **Project Name**: OpenGS-SLAM
- **Description**: AI_Robot_SLAM
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2025-04-01
- **Last Updated**: 2025-04-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README



<p align="center">
  <h1 align="center"> RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes
  </h1>
  <p align="center">
    <a ><strong>Sicheng Yu*</strong></a>
    ·
    <a ><strong>Chong Cheng*</strong></a>
    ·
    <a ><strong>Yifan Zhou</strong></a>
    ·
    <a ><strong>Xiaojun Yang</strong></a>
    ·
    <a href="https://wanghao.tech//"><strong>Hao Wang✉</strong></a>
  </p>
  <p align="center">The Hong Kong University of Science and Technology (GuangZhou)</p>
  <p align="center">(* Equal Contribution)</p>

  <h3 align="center"> ICRA 2025</h3>

[[Project page](https://3dagentworld.github.io/opengs-slam/)],[[arxiv](https://arxiv.org/abs/2502.15633)]  



# Getting Started

## Installation

1. Clone OpenGS-SLAM.

```bash
git clone https://github.com/3DAgentWorld/OpenGS-SLAM.git --recursive
cd OpenGS-SLAM
```

2. Setup the environment.

```bash
conda env create -f environment.yml
conda activate opengs-slam
```

3. Compile submodules for Gaussian splatting

```bash
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
```

4. Compile the cuda kernels for RoPE (as in CroCo v2 and DUSt3R).

```bash
cd croco/models/curope/
python setup.py build_ext --inplace
cd ../../../
```

Our test setup was:

- Ubuntu 20.04: `pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 cudatoolkit=11.8`
- NVIDIA RTX A6000

## Checkpoints

You can download the *'<u>DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth</u>'* checkpoint from the [DUSt3R](https://github.com/naver/dust3r) code repository, and save it to the 'checkpoints' folder.

Alternatively, download it directly using the following method:

```bash
mkdir -p checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/DUSt3R/DUSt3R_ViTLarge_BaseDecoder_512_dpt.pth -P checkpoints/
```

Please note that you must agree to the DUSt3R license when using it.

## Downloading Datasets

The processed data for the 9 Waymo segments can be downloaded via [baidu](https://pan.baidu.com/s/1I1rnB6B8k2d4wzcRMT6gjA?pwd=omcg ) or [google](https://drive.google.com/drive/folders/1xUyNuNzUtsvZIV_q5Qz9zIXMGoMbLuCr?usp=sharing).

Save data under the `datasets/waymo` directory.

## Run

```bash
## Taking 100613 as an example
CUDA_VISIBLE_DEVICES=0 python slam.py --config configs/mono/waymo/100613.yaml

## All 9 Waymo scenes
bash run_waymo.sh
```

## Demo

- If you want to view the real-time interactive SLAM window, please change `Results-use_gui` in `base_config.yaml` to True.

- When running on an Ubuntu system, a GUI window will pop up.

## Run on other dataset

- Please organize your data format and modify the code in `utils/dataset.py`.

- Depth map input interface is still retained in the code, although we didn't use it for SLAM.

# Acknowledgement

- This work is built on [3DGS](https://github.com/graphdeco-inria/gaussian-splatting),  [MonoGS](https://github.com/muskie82/MonoGS),  and [DUSt3R](https://github.com/naver/dust3r), thanks for these great works.

- For more details about Demo, please refer to [MonoGS](https://github.com/muskie82/MonoGS), as we are using its visualization code.

# Citation

If you found this code/work to be useful in your own research, please considering citing the following:

```bibtex
@article{yu2025rgb,
  title={Rgb-only gaussian splatting slam for unbounded outdoor scenes},
  author={Yu, Sicheng and Cheng, Chong and Zhou, Yifan and Yang, Xiaojun and Wang, Hao},
  journal={arXiv preprint arXiv:2502.15633},
  year={2025}
}

```