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.
Fangcheng Zhu 朱方程， Yunfan Ren 任云帆
Related paper is available on arxiv: Swarm-LIO2.
The accompanying video of Swarm-LIO2 is available on YouTube and Bilibili:
Our paper is currently under review, our code and datasets will be released once the paper is accepted.
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)
