BaiLiping / CGE

[IROS 2024]: Collaborative graph exploration with reduced pose-SLAM uncertainty via submodular optimization.

CGE

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.

Update 07/2024

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}, 
}

Source code will be released soon!

Results

Following are the robot's trajectories with (right) & without (left) active loop-closings.