NGEL-SLAM

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NGEL-SLAM: Neural Implicit Representation-based Global Consistent Low-Latency SLAM System
Yunxuan Mao, Xuan Yu, Kai Wang, Yue Wang, Rong Xiong, Yiyi Liao
ICRA 2024

Installation

Please follow the instructions below to install the repo and dependencies.

mkdir catkin_ws && cd catkin_ws
mkdir src && cd src
git clone https://github.com/YunxuanMao/ngel_slam.git
cd ..
catkin_make

See orb-slam3-ros for more detail.

Install the environment

conda create -n ngel python=3.8
conda activate ngel

pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu113/torch_stable.html

pip install -r requirements.txt

cd yx_kaolin
python setup.py develop

What's more, kaolin-wisp should be installed.

Data Preparation

You should put your data in data folder follow NICE-SLAM and generate a rosbag for ORB-SLAM3

python write_bag.py --input_folder '{PATH_TO_INPUT_FOLDER}' --output '{PATH_TO_ROSBAG}' --frame_id 'FRAME_ID_TO_DATA'

You should change the intrinsics manually in write_bag.py.

Run

You should first start the ORB-SLAM3-ROS, and then using code below

python main.py --config '{PATH_TO_CONFIG}'  --input_folder '{PATH_TO_INPUT_FOLDER}' --output '{PATH_TO_OUTPUT}'

Citation

If you find our code or paper useful for your research, please consider citing:

@article{mao2023ngel,
  title={Ngel-slam: Neural implicit representation-based global consistent low-latency slam system},
  author={Mao, Yunxuan and Yu, Xuan and Wang, Kai and Wang, Yue and Xiong, Rong and Liao, Yiyi},
  journal={arXiv preprint arXiv:2311.09525},
  year={2023}
}

For large scale mapping work, you can refer to NF-Atlas.

zzxfft/ngel_slam