# GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats

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GigaSLAM is a groundbreaking monocular SLAM framework designed for kilometer-scale outdoor environments. It leverages hierarchical Gaussian splats and neural networks to achieve efficient, scalable mapping and high-fidelity rendering. This system addresses the challenges of large-scale tracking and mapping using only RGB input, extending the applicability of Gaussian Splatting SLAM to unbounded outdoor scenes.

GitHub: https://github.com/DengKaiCQ/GigaSLAM
OSRepos URL: https://osrepos.com/repo/dengkaicq-gigaslam

## Summary

GigaSLAM is a groundbreaking monocular SLAM framework designed for kilometer-scale outdoor environments. It leverages hierarchical Gaussian splats and neural networks to achieve efficient, scalable mapping and high-fidelity rendering. This system addresses the challenges of large-scale tracking and mapping using only RGB input, extending the applicability of Gaussian Splatting SLAM to unbounded outdoor scenes.

## Topics

- SLAM
- Monocular SLAM
- Gaussian Splatting
- NeRF
- Computer Vision
- Robotics
- C++
- Deep Learning

## Repository Information

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## Content

## Introduction

GigaSLAM introduces the first RGB NeRF / 3D Gaussian Splatting (3DGS)-based SLAM framework specifically engineered for kilometer-scale outdoor environments. Traditional Neural Radiance Fields (NeRF) and 3DGS SLAM methods are typically confined to smaller, bounded indoor settings. GigaSLAM overcomes these limitations by employing a hierarchical sparse voxel map representation, where Gaussians are decoded by neural networks at multiple levels of detail. This innovative design enables efficient, scalable mapping and high-fidelity viewpoint rendering across expansive, unbounded scenes, as demonstrated on challenging datasets like KITTI, KITTI 360, 4 Seasons, and A2D2.

## Installation

To get GigaSLAM up and running, follow these steps. Note that the project relies on CUDA/C++ components and benefits from a robust hardware environment, ideally with 32+ GiB CPU RAM for processing ultra-long sequences.

### 1. Hardware and System Environment

The project was developed and tested on systems with NVIDIA RTX 4090/L20 GPUs, Intel Xeon CPUs, and Ubuntu 22.04.3 LTS with CUDA 11.8.

### 2. Environment Setup

First, create a conda virtual environment and install PyTorch (version 2.2.0 with CUDA 11.8 is recommended):

bash
conda create -n gigaslam python=3.10
conda activate gigaslam
pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt


Special attention should be paid to `torch_scatter` and `xformers` dependencies. If `torch_scatter` installation fails, manually download and install the wheel from the PyTorch Geometric website. For `xformers-0.0.24`, ensure compatibility with `PyTorch 2.2.0` to avoid forced uninstallation of your PyTorch version.

### 3. Compiling CUDA/C++ Modules

Install the necessary CUDA/C++ components:

*   **3D GS Rendering Module:**
    bash
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
    
*   **Loop-Closure Detection Module:** Install `libopencv-dev`, compile and install `DBoW2`, then install `DPRetrieval`.
    bash
sudo apt-get install -y libopencv-dev
cd DBoW2
mkdir -p build && cd build
cmake ..
make
sudo make install
cd ../..
pip install ./DPRetrieval
    
*   **Loop-Closure Correction Module:**
    bash
python setup.py install
    

### 4. Bag of Words Model Setup

Download and extract the pre-trained Bag of Words vocabulary for `DBoW2`:

bash
wget https://github.com/UZ-SLAMLab/ORB_SLAM3/raw/master/Vocabulary/ORBvoc.txt.tar.gz
tar -xzvf ORBvoc.txt.tar.gz


## Examples

To run GigaSLAM, you first need to modify the `.yaml` configuration files located in the `./config` directory. Update the `Dataset` path to your local dataset (in PNG or JPG format) and specify the camera intrinsics (`fx`, `fy`, `cx`, `cy`).

For instance, an example configuration might look like this:

yaml
Dataset:
  color_path: "/media/deng/Data/4SeasonsDataset/BusinessCampus_recording_2020-10-08_09-30-57/undistorted_images/cam0"
  Calibration:
    fx: 501.4757919305817


Once configured, execute the SLAM process using the following command:

bash
python slam.py --config ./configs/kitti_06.yaml


Pretrained weights for `DISK`, `LightGlue`, and `UniDepth` will be automatically downloaded on the first execution. If you encounter issues loading `UniDepth` models from HuggingFace due to network restrictions, you can try setting `export HF_ENDPOINT=https://hf-mirror.com` or manually download the model weights and specify their local path in the configuration.

If `['SLAM']['viz']` is set to `True` in your `.yaml` file, you will observe real-time visualization outputs in the `results/your_exps/` directory during execution, showcasing the tracking and mapping process.

## Why Use GigaSLAM?

GigaSLAM offers significant advantages for researchers and developers working in computer vision and robotics:

*   **Unprecedented Scale**: It is the first RGB NeRF/3DGS-based SLAM system capable of operating in kilometer-scale outdoor environments, a major breakthrough for this class of methods.
*   **High-Fidelity Mapping**: Utilizes hierarchical Gaussian splats and neural networks to achieve highly detailed and visually faithful 3D reconstructions.
*   **Robust Tracking**: Employs a sophisticated front-end tracking mechanism combining a metric depth model, epipolar geometry, PnP algorithms, and a Bag-of-Words-based loop closure for accurate and consistent pose estimation over long trajectories.
*   **Efficiency and Scalability**: The hierarchical sparse voxel map representation ensures efficient data handling and scalability, making it suitable for large and unbounded scenes.
*   **Monocular Input**: Achieves impressive results using only monocular RGB input, reducing sensor complexity and cost.

## Links

*   GitHub Repository: [GigaSLAM GitHub](https://github.com/DengKaiCQ/GigaSLAM){:target="_blank"}
*   arXiv Paper: [GigaSLAM: Large-Scale Monocular SLAM with Hierarchical Gaussian Splats](https://arxiv.org/abs/2503.08071){:target="_blank"}