This repo implements a SLAM-Aware Collaborative Graph Exploration (CGE) method, which finds quick coverage path for multiple robots, while forming a well-connected collaborative pose graph to reduce SLAM uncertainty. Approximation algorithms in submodular maximization are adopted to provided performance guarantees for the actively selected loop-closing actions (loop closures).
This work extends our previous work on single-robot SLAM-aware exploration to the multi-robot case. Follow this IEEE RA-L paper and open-sourced code for more details.
Our paper has been accpeted by IEEE/RSJ IROS 2024 !!!
Please follow this link to the Arxiv version. Please consider citing our paper if you find it helpful.
@misc{bai2024collaborativegraphexplorationreduced,
title={Collaborative Graph Exploration with Reduced Pose-SLAM Uncertainty via Submodular Optimization},
author={Ruofei Bai and Shenghai Yuan and Hongliang Guo and Pengyu Yin and Wei-Yun Yau and Lihua Xie},
year={2024},
eprint={2407.01013},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2407.01013},
}
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Install python libraries
networkx,scipy,statistics,pickle,pyyaml. They can be installed by usingpip install xxx. -
Install OR-Tools for python:
python -m pip install ortools.
- Specify save path in
config.yaml - Run
main.py - Visualize the results by running
simulation.py. The code will read results from paths specified inconfig.yaml.
Following are the robot's trajectories with (right) & without (left) active loop-closings.
