# [ICRA2024]Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases
First Author: [**Jie Yin 殷杰**](https://sjtuyinjie.github.io/)   📝 [[Paper]](https://ieeexplore.ieee.org/document/10610070) / [[Arxiv]](https://arxiv.org/abs/2402.14308)   ➡️ [[Dataset]](https://github.com/SJTU-ViSYS/M2DGR-plus)   ⭐️ [[Presentation Video]](https://www.bilibili.com/video/BV1xx421m75k/?spm_id_from=333.337.search-card.all.click&vd_source=0804300aea4065df90adde5398ee74b7)   🔥[[News]](https://mp.weixin.qq.com/s/CfnfxHvn9pbYc4599_JoSg) [![Author](https://img.shields.io/badge/Author-Jie%20Yin-blue)](https://sjtuyinjie.github.io/) [![Paper](https://img.shields.io/badge/Paper-GroundFusion-yellow)](https://ieeexplore.ieee.org/document/10610070) [![Preprint](https://img.shields.io/badge/Ppreprint-Arxiv-purple)](https://arxiv.org/abs/2112.13659/) [![Dataset](https://img.shields.io/badge/Dataset-M2DGR+%2B-green)](https://github.com/SJTU-ViSYS/M2DGR-plus) [![License](https://img.shields.io/badge/License-GPLv3-cyan)]() [![Video](https://img.shields.io/badge/Video-red)](https://www.bilibili.com/video/BV1xx421m75k/?spm_id_from=333.337.search-card.all.click&vd_source=0804300aea4065df90adde5398ee74b7) [![News](https://img.shields.io/badge/News-orange)](https://mp.weixin.qq.com/s/CfnfxHvn9pbYc4599_JoSg) [![stars](https://img.shields.io/github/stars/SJTU-ViSYS/Ground-Fusion.svg)](https://github.com/SJTU-ViSYS/Ground-Fusion) [![forks](https://img.shields.io/github/forks/SJTU-ViSYS/Ground-Fusion.svg)](https://github.com/SJTU-ViSYS/Ground-Fusion) [![open issues](https://img.shields.io/github/issues-raw/SJTU-ViSYS/Ground-Fusion)](https://github.com/SJTU-ViSYS/Ground-Fusion/issues) [![issue resolution](https://img.shields.io/github/issues-closed-raw/SJTU-ViSYS/Ground-Fusion)](https://github.com/SJTU-ViSYS/Ground-Fusion/issues)
[![ICRA2024 Presentation](cover.jpg)](https://www.bilibili.com/video/BV1xx421m75k/?spm_id_from=333.337.search-card.all.click&vd_source=0804300aea4065df90adde5398ee74b7) > [!TIP] > Check out the [**presentation video above**](https://www.bilibili.com/video/BV1xx421m75k/?spm_id_from=333.337.search-card.all.click&vd_source=0804300aea4065df90adde5398ee74b7) for a quick overview of this work! ## News - **🔥`2025/06/16`**: Introducing our new paper on IROS2025:"Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework" is accepted to IROS2025!code: **[Ground-Fusion++](https://github.com/sjtuyinjie/Ground-Fusion2)**, dataset: **[M3DGR](https://github.com/sjtuyinjie/M3DGR)**! ## NOTICE ### We warmly welcome and highly recommend the integration of the Ground-Fusion system into your projects for several compelling reasons: 1. **🔥Comprehensive Sensor Suite**: The Ground-Fusion system is equipped with a multitude of sensors (RGBD-IMU-Wheel-GNSS), facilitating an easy onset for enhancements to any module. This richness in sensory input streamlines the process of adapting and refining components within the system. 2. **⭐️Open-Source Ecosystem**: Both the Ground-Fusion algorithm and associated datasets such as [**M2DGR-plus**](https://github.com/SJTU-ViSYS/M2DGR-plus) and the [**Ground-Challenge**](https://github.com/sjtuyinjie/Ground-Challenge) are openly available, forming a comprehensive bechmark suite. **Welcome to beat Ground-Fusion on M2DGR and Ground-Challenge!** 3. **🚀Proven Performance**: The Ground-Fusion algorithm has been rigorously validated across diverse datasets, establishing itself as SOTA in lidar-less SLAM algorithms. Outperforming Ground-Fusion on these benchmarks would significantly bolster the credibility of your proposed method. ## Introduction We introduce Ground-Fusion, a low-cost sensor fusion simultaneous localization and mapping (SLAM) system for ground vehicles. Our system features efficient initialization, effective sensor anomaly detection and handling, real-time dense color mapping, and robust localization in diverse environments. We tightly integrate RGB-D images, inertial measurements, wheel odometer and GNSS signals within a factor graph to achieve accurate and reliable localization both indoors and outdoors. To ensure successful initialization, we propose an efficient strategy that comprises three different methods: stationary, visual, and dynamic, tailored to handle diverse cases. Furthermore, we develop mechanisms to detect sensor anomalies and degradation, handling them adeptly to maintain system accuracy. The preprint version of paper is [here](http://arxiv.org/abs/2402.14308). The dataset is at [**M2DGR-plus**](https://github.com/SJTU-ViSYS/M2DGR-plus), [**Ground-Challenge**](https://github.com/sjtuyinjie/Ground-Challenge) and [**M2DGR**](https://github.com/SJTU-ViSYS/M2DGR).

Figure 1. We categorize corner cases into three types: visual, wheel, and GNSS challenges.

## ⚙️ 1. Prerequisites & Installation ### 1.1 OS & ROS Support ✅ Ubuntu 18.04 + ROS Melodic + OpenCV3 ✅ Ubuntu 20.04 + ROS Noetic + OpenCV4 ### 1.2 OpenCV This package requires [OpenCV 3/4](https://github.com/opencv/opencv) and some features of C++11. ### 1.3 Eigen, Ceres, and PCL This package requires [Eigen 3.3.7](https://github.com/PX4/eigen), [Ceres 1.14](https://ceres-solver.googlesource.com/ceres-solver),[Sophus](https://github.com/strasdat/Sophus.git ) and [PCL 1.10 or 1.11](https://github.com/PointCloudLibrary/pcl). You need to download they in your thirdparty folder, and then: ~~~ sudo apt-get update sudo apt-get install -y cmake libgoogle-glog-dev libgflags-dev libatlas-base-dev libsuitesparse-dev cd thirdparty/eigen mkdir -p build && cd build cmake .. sudo make install cd ../../ceres-solver mkdir -p build && cd build cmake .. make -j$(nproc) sudo make install sudo apt-get install -y libflann-dev libvtk6-dev libboost-all-dev ros-noetic-pcl-ros (for ubuntu20.04) libfmt-dev cd ../../pcl mkdir -p build && cd build cmake .. make -j$(nproc) sudo make install cd ../../Sophus mkdir -p build && cd build cmake .. make -j$(nproc) sudo make install ~~~ ### 1.4 Gnss_comm This package also requires [gnss_comm](https://github.com/HKUST-Aerial-Robotics/gnss_comm) for ROS message definitions and some utility functions. ### 1.5 Configure gcc (For Ubuntu 20.04) ~~~ sudo apt-get install g++-8 sudo apt-get install gcc-8 sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 20 sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 20 ~~~ ### 1.6 Build Ground-Fusion After install all 3rd parties： ~~~ mkdir -p ~/Groundfusion_ws/src cd ~/Groundfusion_ws/src git clone https://github.com/HKUST-Aerial-Robotics/gnss_comm git clone https://github.com/SJTU-ViSYS/Ground-Fusion cd ../.. catkin_make -j12 ~~~ > 💡 Tip: If you encounter `Sophus` issues, try building both template and non-template versions. ## 🚀 2. Run Examples ### 2.1 Ground-challenge dataset Download [Ground-challenge](https://github.com/sjtuyinjie/Ground-Challenge) dataset and give a star. ~~~ # [launch] open a terminal and type: source devel/setup.bash roslaunch vins groundfusion.launch # [run localization] open another terminal: source devel/setup.bash rosrun vins vins_node src/Ground-Fusion/config/realsense/groundchallenge.yaml # [dense map]open third terminal: source devel/setup.bash rosrun dense_map dense_map_node src/Ground-Fusion/config/realsense/groundchallenge.yaml # [play rosbag]open forth terminal: rosbag play office3.bag ~~~ > ⚠️ Mapping node is resource-intensive, affecting the real-time performance of the entire system. So it's suggested that do not run this node unless necessary. We are working on optimizing the mapping node currently ### 2.2 M2DGR-Plus dataset Download [M2DGR-Plus](https://github.com/sjtuyinjie/M2DGR-plus) dataset and give a star. ~~~ # [launch] open a terminal and type: source devel/setup.bash roslaunch vins groundfusion.launch # [run localization] open another terminal: source devel/setup.bash rosrun vins vins_node src/Ground-Fusion/config/realsense/m2dgrp.yaml # [dense map]open third terminal: source devel/setup.bash rosrun dense_map dense_map_node src/Ground-Fusion/config/realsense/m2dgrp.yaml # [play rosbag]open forth terminal: rosbag play anamoly.bag ~~~ > ⚠️ **Known Issues**: > - Ground-Fusion may perform better *without GNSS* on M2DGR-Plus due to its low (1Hz) GNSS frequency. > - Initialization thresholds may need tuning to avoid early-phase drift. > - A more advanced version of Ground-Fusion is under development — stay tuned! --- ## 🙏 3.Acknowledgements This work is supported by: - National Key R&D Program (2022YFB3903802) - NSFC (62073214) - Midea Group > This project is built on [GVINS](https://github.com/HKUST-Aerial-Robotics/GVINS), with inspirations from [VIW-Fusion](https://github.com/TouchDeeper/VIW-Fusion) and [VINS-RGBD](https://github.com/STAR-Center/VINS-RGBD) — huge thanks! --- ## 📄 4. Citation ```bibtex @inproceedings{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}, booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, pages={8603--8609}, year={2024}, organization={IEEE} } @article{zhang2025towards, title={Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework}, author={Zhang, Deteng and Zhang, Junjie and Sun, Yan and Li, Tao and Yin, Hao and Xie, Hongzhao and Yin, Jie}, journal={arXiv preprint arXiv:2507.08364}, year={2025} } @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} } @inproceedings{yin2023ground, title={Ground-challenge: A multi-sensor slam dataset focusing on corner cases for ground robots}, author={Yin, Jie and Yin, Hao and Liang, Conghui and Jiang, Haitao and Zhang, Zhengyou}, booktitle={2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)}, pages={1--5}, year={2023}, organization={IEEE} } ``` ## ⭐️ 5. Star History [![Star History Chart](https://api.star-history.com/svg?repos=SJTU-ViSYS/Ground-Fusion&type=Timeline)](https://star-history.com/#Ashutosh00710/github-readme-activity-graph&Timeline) Thanks to everyone for star this project. github-stargazers

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