NGD-SLAM

This is a visual SLAM system designed for dynamic environments, based on the ORB-SLAM3 framework. It runs in real-time on a single laptop CPU without compromising tracking accuracy [Demo] [Paper].

1. Prerequisites

The system is tested on Ubuntu 20.04 and 22.04, and it should be easy to compile in other platforms. A powerful computer will provide more stable and accurate results.

C++11 or C++0x Compiler

It uses the new thread and chrono functionalities of C++11.

Pangolin

It uses Pangolin for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.

OpenCV

It uses OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at leat 4.4.

Eigen3

Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.

YOLO (Included in Thirdparty folder)

It uses the C++ version of the YOLO-fastest model. The model configuration and pre-trained weights are included in the Thirdparty folder and are loaded using OpenCV.

DBoW2 and g2o (Included in Thirdparty folder)

It uses modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.

Python

Required to calculate the alignment of the trajectory with the ground truth. Required Numpy module.

• (win) http://www.python.org/downloads/windows
• (deb) sudo apt install libpython2.7-dev
• (mac) preinstalled with osx

2. Building NGD-SLAM

Clone the repository:

git clone https://github.com/yuhaozhang7/NGD-SLAM.git NGD-SLAM

It provides a script build.sh to build the libraries. Please make sure you have installed all required dependencies. Execute:

cd NGD-SLAM
chmod +x build.sh
./build.sh

3. Running TUM Examples

TUM dataset includes sequences that are captured using an RGB-D camera in dynamic environments. Download the desired sequence and uncompress it. Below is an example command for the freiburg3_walking_xyz sequence:

./Examples/RGB-D/rgbd_tum ./Vocabulary/ORBvoc.txt ./Examples/RGB-D/TUM3.yaml ./path/to/TUM/rgbd_dataset_freiburg3_walking_xyz ./Examples/RGB-D/associations/fr3_walk_xyz.txt

4. Citation

If you find this work to be useful in your research, please consider citing:

@article{zhang2024ngdslam,
  title={{NGD-SLAM}: Towards Real-Time SLAM for Dynamic Environments without GPU},
  author={Zhang, Yuhao},
  journal={arXiv preprint arXiv:2405.07392},
  year={2024}
}