NDTMC-LIO-SAM

The paper is already available at NDT-Map-Code.

A real-time lidar-inertial SLAM package.

• This repository is a SLAM method combined with NDTMC and LIO-SAM, which enables Robust loop closure detection to eliminate accumulated errors.

News

• [2024/2]🔥 NDT-Map-Code is accepted by IROS 2024.

Demo

Example

Our method is tested on the open source dataset KITTI.

The matching results of our method on the three sequences of KITTI 02, 07 and 08 are:

On the basis of the same threshold setting, the matching results of SC-LIO-SAM are:

When the similarity threshold is 0.6, the trajectory comparison figure is as follows:

and the time required by the two methods is compared:

How to use?

• You can download the KITTI, and then you can complete the self-test according to the following steps:

Place the directory NDTMC-LIO-SAM under user catkin work space

For example,

```
cd ~/catkin_ws
git clone https://github.com/SlamCabbage/NDTMC-LIO-SAM.git
cd ..
catkin_make
source devel/setup.bash
```

After compiling, you can use the script we provide to run the program,

```
launch_file = 'path/to/run.launch'
bag_files = [
    'path/to/.bag'
]
cd src/NDTMC-LIO-SAM/script
python3 autoRun.py
rviz -d src/NDTMC-LIO-SAM/launch/include/config/rviz.rviz
```

Dependency

• All dependencies are same as the original LIO-SAM
• Additional installation of ceres is required:

# CMake
sudo apt-get install cmake
# google-glog + gflags
sudo apt-get install libgoogle-glog-dev libgflags-dev
# Use ATLAS for BLAS & LAPACK
sudo apt-get install libatlas-base-dev
# Eigen3
sudo apt-get install libeigen3-dev
# SuiteSparse (optional)
sudo apt-get install libsuitesparse-dev
git clone https://ceres-solver.googlesource.com/ceres-solver
cd ceres-solver
git checkout -b build 1.14.0
mkdir build
cd build
cmake ..
make -j4
sudo make install

Cite NDTMC-LIO-SAM

@misc{liao2023ndtmapcode,
      title={NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM}, 
      author={Lizhou Liao and Li Sun and Xinhui Bai and Zhenxing You and Hongyuan Yuan and Chunyun Fu},
      year={2023},
      eprint={2307.08221},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

and

@inproceedings{liosam2020shan,
  title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},
  author={Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={5135-5142},
  year={2020},
  organization={IEEE}
}

and

@inproceedings { gkim-2018-iros,
 author = {Kim, Giseop and Kim, Ayoung},
 title = { Scan Context: Egocentric Spatial Descriptor for Place Recognition within {3D} Point Cloud Map },
 booktitle = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
 year = { 2018 },
 month = { Oct. },
 address = { Madrid }
}

Contact

• Maintainer: Lizhou Liao ([email protected])

Contributors

• Lizhou Liao: completed the code

Acknowledgement

• Thanks for SC-LIO-SAM.
• Thanks for NIO low-speed localization and mapping group.