SLAMesh

SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing

This is the code of SLAMesh.

You are welcome to visit my personal repository and raise issues there so that I can receive immediate notifications. I may also update it more frequently but sometimes the result may slightly differ from the paper.

Update

17/Aug/2023, Code released. Feel free to contact me with any questions. We are confident that this work introduced a novel approach to LiDAR SLAM, and we welcome everyone to explore opportunities in this approach. :two_men_holding_hands:

10/Mar/2023, Preprint of our paper can be found on: paper, slides.

16/Jan/2023, The paper has been accepted for presentation on ICRA 2023.

15/Sep/2022, The paper has been submitted to ICRA 2023.

1. Introduction

This work designs a Simultaneously Localization And Meshing system (SLAMesh). Mesh is a lightweight 3-D dense model. It can model complex structures and has the feasibility for rendering. We bridge localization with meshing at the same time to benefit each other.

1.1 Main features

• Build, register, and update the mesh maps directly in real-time with CPU resources, around 40 Hz.
• Provide accurate odometry. Kitti odometry benchmark: 0.6763%.
• Established a new approach to LiDAR SLAM different from point-cloud, NDT, and surfel map SLAM.
• Continuous mapping with uncertainty via a reconstruction. Fast meshing, matching without kd-tree.

1.2 Demo video

On public dataset:

(or watch it on bilibili)

On self-collected dataset:

(or watch it on bilibili)

1.3 Find more detail

If you find our research helpful to your work, please cite our paper:

[1] Jianyuan Ruan, Bo Li, Yibo Wang, and Yuxiang Sun, "SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing" ICRA 2023 (pdf, IEEE, slides.).

@INPROCEEDINGS{10161425,
  author={Ruan, Jianyuan and Li, Bo and Wang, Yibo and Sun, Yuxiang},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing}, 
  year={2023},
  volume={},
  number={},
  pages={3546-3552},
  doi={10.1109/ICRA48891.2023.10161425}}

Other related papers:

[2] Jianyuan Ruan, Bo Li, Yinqiang Wang and Zhou Fang, "GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction," IROS 2020. link.

[3] Bo Li, Yinqiang Wang, Yu Zhang. Wenjie Zhao, Jianyuan Ruan, and Pin Li, "GP-SLAM: laser-based SLAM approach based on regionalized Gaussian process map reconstruction". Autonomous Robot 2020.link

If you understand Chinese, you can also refer to my Master's thesis, an article on the WeChat platform: SLAMesh: 实时LiDAR定位与网格化模型构建 , and a talk in 自动驾驶之心and计算机视觉life.

2. Build

2.1 Prerequisite

We tested our code in Ubuntu18.04 with ROS melodic and Ubuntu20.04 with ROS neotic.

ROS

Install ros following ROS Installation. We use the PCL and Eigen library in ROS.

Ceres

Install Ceres Solver, version 2.0 or 2.1. follow Ceres Installation.

Notice that version > 2.1 may have some compatibility issues with our code. So you can use following command to install ceres:

apt-get install cmake libgoogle-glog-dev libgflags-dev libatlas-base-dev libsuitesparse-dev -y
git clone https://github.com/ceres-solver/ceres-solver.git -b 2.1.0
mkdir ceres-bin && cd ceres-bin && cmake .. && make -j$(($(nproc)-2))
make install

mesh_tools

We use mesh_tools to visualize the mesh map with the mesh_msgs::MeshGeometryStamped ROS message. mesh_tools also incorporates navigation functions upon mesh map. Mesh tool introduction

Install mesh_tools by:

1. Install lvr2:

   sudo apt-get install build-essential \
    cmake cmake-curses-gui libflann-dev \
    libgsl-dev libeigen3-dev libopenmpi-dev \
    openmpi-bin opencl-c-headers ocl-icd-opencl-dev \
    libboost-all-dev \
    freeglut3-dev libhdf5-dev qtbase5-dev \
    qt5-default libqt5opengl5-dev liblz4-dev \
    libopencv-dev libyaml-cpp-dev

   If in Ubuntu18.04, use libvtk6 because libvtk7 will conflict with pcl-ros in melodic.

   sudo apt-get install  libvtk6-dev libvtk6-qt-dev

   Else if, in Ubuntu 20.04,

   sudo apt-get install  libvtk7-dev libvtk7-qt-dev

   End of IF

then:

cd a_non_ros_dir

build:

git clone https://github.com/uos/lvr2.git
cd lvr2 
mkdir build && cd build
cmake .. && make
sudo make install

It may take you some time.

2. Install mesh_tools, (I can not install it from official ROS repos now, so I build it from source)

cd slamesh_ws/src
git clone https://github.com/naturerobots/mesh_tools.git
cd ..
rosdep update
rosdep install --from-paths src --ignore-src -r -y
catkin_make
source devel/setup.bash

2.2 SLAMesh

Clone this repository and build:

cd slamesh_ws/src
git clone https://github.com/RuanJY/SLAMesh.git
cd .. && catkin_make
mkdir slamesh_result
source ~/slamesh_ws/src/devel/setup.bash

2.3 Docker support

If you encounter some troubles when building slamesh, the problem may lay down on the prerequisite; we advise you to use the docker image:

Option 1, Build the docker image use Dockerfile:

cd slamesh_ws/src/SLAMesh/docker/
chmod +x run_docker.sh
./run_docker.sh -b

Run the docker image:

docker run -it --rm     --gpus=all     --runtime=nvidia     -e NVIDIA_DRIVER_CAPABILITIES=all     --env="DISPLAY=$DISPLAY" \
-e "QT_X11_NO_MITSHM=1"     --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw"     --env="XAUTHORITY=$XAUTH" --volume="$XAUTH:$XAUTH" \
--net=host     --privileged \
--volume=path_of_dataset_in_your_PC:/home/ruanjy/Dataset/ \
--volume=/home/$USER/slamesh_result:/home/slamesh/slamesh_ws/slamesh_result \
--name=test_slamesh    slamesh     bash

change the path_of_dataset_in_your_PC to the folder path of the dataset in your PC, like --volume=/home/ruanjy/kitti_odometry/data_odometry_velodyne/dataset/:/home/ruanjy/Dataset/ \

In this way, SLAMesh are built already, so source the workspace and run the launch file. You can also play rosbag in your host PC.

Option 2, otherwise, you can also pull the pre-built docker image with VNC directly:

docker pull pleaserun/rjy_slam_work:slamesh_18.04

Run the docker image:

docker run -it -p 5900:5900 -p 2222:22 -e RESOLUTION=1920x1080  \
--volume=path_of_dataset_in_your_PC:/home/ruanjy/Dataset/ \
--volume=/home/$USER/slamesh_result:/home/slamesh/slamesh_ws/slamesh_result \
--name test_slamesh pleaserun/rjy_slam_work:slamesh_18.04

change the path_of_dataset_in_your_PC to the folder path of the dataset in your PC, like --volume=/home/ruanjy/kitti_odometry/data_odometry_velodyne/dataset/:/home/ruanjy/Dataset/ \

In this way you can use VNC to enter a graphical interface via port 5900, or use ssh to connect container via port 2222.

Move to the dictionary slamesh_ws/src, and complete step from 2.2.

3. Usage

3.1 Run kitti dataset:

The dataset is available at KITTI dataset.

Set the parameter data_path in slamesh_kitti.launch to your folder of kitti dataset path

The file tree should be like this:

file_loc_dataset
    ├── 00
    |   └──velodyne
    |       ├── 000000.bin
    |       └── ...
    └──01   

For example, if you want to run the 07 sequence:

roslaunch slamesh slamesh_kitti_meshing.launch seq:=/07

You should get:

If you can not see the mesh, check that the Rviz plugin is sourced correctly. When mesh_visualization is disabled, only vertices are published as a point cloud.

3.2 Run Mai City dataset:

The dataset is available at Mai City Dataset. Sequence 01 can be fed into SLAM and sequence 02 can be accumulated into a dense ground truth point cloud map.

Similarly, set the parameter data_path in slamesh_maicity.launch to your folder of the kitti dataset path.

roslaunch slamesh slamesh_maicity.launch seq:=/01

You should get:

3.3 Run online or ros bag

roslaunch slamesh slamesh_online.launch

And play your bag, in launch file, remap the topic "/velodyne_points" to your LiDAR topic like "/os_cloud_node/points".

rosbag play your_bag.bag 

The number of LiDAR channels does not matter because our algorithm does not extract features.

You can use our sample data recorded with an Ouster OS1-32 LiDAR: SLAMesh dataset.

3.4 About visualization

Because mesh-tools rviz plugin do not support incremental mesh intersection, we provide three visualization modes controlled by visualisation_type in the param.yaml file:

visualisation_type =

lighter ↑

• 0, publish registered raw points in world frame, just like fast-lio, lio sam
• 1, + publish the vertices of mesh as point cloud, each scan + (1/n) map global
• 2, + visualize local updated mesh, each scan
• 3, + visualize global mesh, (1/n) frame

heavy ↓

after finish whole process, the global mesh map will be visualized in any mode

4. Evaluation

SLAMesh saves all its report to the path result_path given in each launch file. If you find ros warning: Can not open Report file, create the folder of result_path first.

4.1 Kitti odometry accuracy

Run SLAMesh by:

roslaunch slamesh slamesh_kitti_odometry.launch seq:=/07

Then SLAMesh produce a file named 0*_pred.txt in the KITTI path format. I use KITTI odometry evaluation tool for evaluation:

cd slamesh_ws/slamesh_result
git clone https://github.com/LeoQLi/KITTI_odometry_evaluation_tool
cd KITTI_odometry_evaluation_tool/
python evaluation.py --result_dir=.. --eva_seqs=07.pred

The result on the KITTI odometry benchmark is:

//: # ()

Why use the slamesh_kitti_odometry.launch ? To achieve better KITTI odometry performance, the parameter in slamesh_kitti_odometry.launch are set as followed:

full_cover: false # due to discontinuity phenomenon between cells, shirnk the test locations can improve accuracy.
num_margin_old_cell: 500 # margin old cells, because KITTI evaluate odometry accuracy rather than consistency.

While in launch slamesh_kitti_meshing.launch, to have better meshing result, they aree:

full_cover: true # so that there is no gap between cells.
num_margin_old_cell: -1  # do not margin old cells, the cell-based map will have implicit loop closure effect.

4.2 Mesh quality

To save the mesh map, set parameter save_mesh_map in yaml file to true. A ***.ply file should be saved in slamesh_ws/slamesh_result.

I use the TanksAndTemples/evaluation tool to evaluate the mesh. I slightly modify it (remove trajectory). You can find it here: TanksAndTemples/evaluation_rjy

Then compare the mesh with the ground-truth point cloud map:

cd TanksAndTemples_direct_rjy/python_toolbox/evaluation/
python run.py \
--dataset-dir ./data/ground_truth_point_cloud.ply \
--traj-path ./ \
--ply-path ./data/your_mesh.ply

4.3 Time cost

The direct meshing method enables SLAMesh 180x faster than Poisson reconstruction based method, Puma, in the Maicity dataset. The time cost of each step is recorded in the ***_report.txt file.

5. Help you to read the code

TODO

Contact

Author: Jianyuan Ruan, Bo Li, Yibo Wang, Yuxiang Sun.

Email: jianyuan.ruan@connect.polyu.hk, 120052@zust.edu.cn, me-yibo.wang@connect.polyu.hk, yx.sun@polyu.edu.hk

Acknowledgement

Most of the code are build from scratch , but we also want to acknowledge the following open-source projects:

TanksAndTemples/evaluation: evaluation

A-LOAM: kitti dataset loader and tic-toc

F-LOAM: help me to write the ceres residuals

VGICP: multi-thread