Fangcheng Zhu, Yunfan REN Longji Yin, Fanze Kong, Qingbo Liu, Ruize Xue, Wenyi Liu, Yixi Cai, Guozheng Lu, Haotian Li, and Fu Zhang
Swarm-LIO2 is a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient LiDAR-inertial odometry for aerial swarm systems.
Our package address following key issues for a UAV swarm system:
- Robust, real-time, accurate ego-state estimation and mutual state estimation.
- High quality global extrinsic calibration.
- Superior computation and communication efficiency which supports large swarm scales.
- Excellent robustness in various scenarios: indoor, outdoor, dark night, degenerate corridors...
- Support diverse UAV swarm applications: target tracking, collaborative exploration, payload transportation...
Swarm-LIO2 improves our previous work Swarm-LIO (see below) mainly in five crucial aspects:
- Fast Initialization: factor graph optimization is utilized for efficient identification and global extrinsic calibration, which largely decreases the complexity and energy consumption of the swarm initialization.
- Efficient Computation: novel marginalization and degeneration evaluation are presented to alleviate computation burden and to support large swarm scales.
- Detailed Modeling: detailed measurement modeling and temporal compensation of the mutual observation measurements are proposed, which mitigates the approximation error when fusing data.
- Comprehensive Experiments: more extensive experiments in both simulated and real-world environments are conducted, which demonstrate superior performances in terms of robustness, efficiency, and wide supportability to diverse aerial swarm applications.
- Open Source: all the system designs will be open-sourced to contribute the robotic society.
Related paper is available on arxiv: Swarm-LIO2.
The accompanying video of Swarm-LIO2 is available on YouTube and Bilibili:
Ubuntu = 20.04.
ROS = Noetic. ROS Installation
PCL >= 1.8, Follow PCL Installation.
Eigen >= 3.3.4, Follow Eigen Installation.
Follow livox_ros_driver Installation or livox_ros_driver2 Installation .
Remarks:
- Since the Swarm-LIO2 must support Livox serials LiDAR firstly, so the livox_ros_driver or livox_ros_driver2 (select correct LiDAR driver according to your LiDAR) must be installed and sourced before run any Swarm-LIO2 luanch file.
sudo apt-get install libboost-all-dev
sudo apt-get install cmake
sudo apt-get install libtbb-dev
Download GTSAM from Swarm-LIO2 data and dependencies, and
mkdir build
cd build
cmake ..
make check (optional, runs unit tests)
make install
sudo cp /usr/local/lib/libgtsam.so.4 /usr/lib
sudo cp /usr/local/lib/libmetis-gtsam.so /usr/lib
Clone the repository and catkin_make:
cd ~/swarm_ws/src
git clone [email protected]:hku-mars/Swarm-LIO2.git
cd ..
catkin_make -j
source devel/setup.bash
The LI-Init: robust real-time LiDAR-IMU initialization toolkit is recommended.
The calibrated extrinsic and temporal offset should be correctly modified in xxx.yaml file.
Edit config/xxx.yaml and fill in the appropriate parameters.
More details on the meanings of the parameters and methods for adjustment will be provided later.
Run the UDP communication module:
cd swarm_ws
source devel/setup.bash
roslaunch udp_bridge udp_online.launch
Run the state estimation module of Swarm-LIO2:
cd swarm_ws
source devel/setup.bash
roslaunch swarm_lio livox_mid360.launchDownload example rosbag (real-world data) -- mutual_avoidance_uav1.bag -- from Swarm-LIO2 data and dependencies, then
cd swarm_ws
source devel/setup.bash
roslaunch swarm_lio livox_mid360.launch
rosbag play mutual_avoidance_uav1.bag
Swarm-LIO is a fully decentralized state estimation method for aerial swarm systems, in which each drone performs precise ego-state estimation, exchanges ego-state and mutual observation information by wireless communication, and estimates relative state with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on LiDAR-inertial measurements.
Our related papers are now available: Swarm-LIO: Decentralized Swarm LiDAR-inertial Odometry
Bibtex format:
@inproceedings{zhu2023swarm,
title={Swarm-lio: Decentralized swarm lidar-inertial odometry},
author={Zhu, Fangcheng and Ren, Yunfan and Kong, Fanze and Wu, Huajie and Liang, Siqi and Chen, Nan and Xu, Wei and Zhang, Fu},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
pages={3254--3260},
year={2023},
organization={IEEE}
}
Our accompanying videos are now available on YouTube and Bilibili (click below images to open)
