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M2DGR-Benchmark

Authors: Junjie Zhang (张骏杰), Deteng Zhang (张德腾), Yan Sun (孙岩), and Jie Yin (殷杰)*

The goal of M2DGR-Benchmark is to benchmark all SLAM systems on M2DGR/M2DGR+ datasets! So we will keep updating state-of-the-art SLAM systems to them.

This project adapts leading LiDAR, Visual, and sensor-fusion SLAM systems to both M2DGR and M2DGR+ dataset, facilitating research and development in SLAM technologies. Detailed installation methods of open-source projects on M2DGR/M2DGR+ are available in the respective project folders.

Furthermore, extensive open-source systems are tested upon M2DGR, such as Ground-Fusion, LVI-SAM-Easyused, MM-LINS, Log-LIO, LIGO, Swarm-SLAM, VoxelMap++, GRIL-Cali, LINK3d, i-Octree, LIO-EKF, Fast-LIO ROS2, HC-LIO, LIO-RF, PIN-SLAM, LOG-LIO2, Section-LIO, I2EKF-LO, Liloc, BMBL, Light-LOAM and so on. Feel free to try these on M2DGR-benchmark!

Example of r3live_on M2DGR door02

LiDAR-based Methods

Vision-based Methods

LiDAR-Visual Fusion Methods

Explore these methods to see how they perform on the M2DGR and M2DGR-plus datasets!

License

If you use this work in an academic work, please cite:

@article{yin2021m2dgr,
  title={M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots},
  author={Yin, Jie and Li, Ang and Li, Tao and Yu, Wenxian and Zou, Danping},
  journal={IEEE Robotics and Automation Letters},
  volume={7},
  number={2},
  pages={2266--2273},
  year={2021},
  publisher={IEEE}
}

@article{yin2024ground,
  title={Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases},
  author={Yin, Jie and Li, Ang and Xi, Wei and Yu, Wenxian and Zou, Danping},
  journal={arXiv preprint arXiv:2402.14308},
  year={2024}
}

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