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| # M2DGR： a Multi-modal and Multi-scenario SLAM Dataset for Ground Robots （RA-L & ICRA2022） | ||
| ## First Author: [**Jie Yin**](https://github.com/sjtuyinjie?tab=repositories) | ||
| <div align=center> | ||
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| ## NOTICE | ||
| ### We strongly recommend that the newly proposed SLAM algorithm be tested on our M2DGR / M2DGR-plus benchmark, because our data has following features: | ||
| 1. A rich pool of sensory information including vision, lidar, IMU, GNSS,event, thermal-infrared images and so on | ||
| 2. Various scenarios in real-world environments including lifts, streets, rooms, halls and so on. | ||
| 3. Our dataset brings great challenge to existing SLAM algorithms including [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM) and [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3). If your proposed algorihm outperforms SOTA systems on [M2DGR](https://github.com/SJTU-ViSYS/M2DGR)/[M2DGR-plus](https://github.com/SJTU-ViSYS/M2DGR-plus), your paper will be much more convincing and valuable. | ||
| 4. A lot of excellent open-source projects have been built on M2DGR/M2DGE-plus, for examples, [Ground-Fusion](https://github.com/SJTU-ViSYS/Ground-Fusion), [Log-LIO](https://github.com/tiev-tongji/LOG-LIO), [Swarm-SLAM](https://github.com/MISTLab/Swarm-SLAM), DAMS-LIO, [VoxelMap++](https://github.com/uestc-icsp/VoxelMapPlus_Public), [GRIL-Cali](https://github.com/SJTU-ViSYS/Ground-Fusion),[LINK3d](https://github.com/YungeCui/LinK3D),[i-Octree](https://github.com/zhujun3753/i-octree),[LIO-EKF](https://github.com/YibinWu/LIO-EKF) and so on! | ||
| 4. 🔥 A lot of excellent open-source projects have been built on M2DGR/M2DGE-plus, for examples, [Ground-Fusion](https://github.com/SJTU-ViSYS/Ground-Fusion), [Log-LIO](https://github.com/tiev-tongji/LOG-LIO), [Swarm-SLAM](https://github.com/MISTLab/Swarm-SLAM), DAMS-LIO, [VoxelMap++](https://github.com/uestc-icsp/VoxelMapPlus_Public), [GRIL-Cali](https://github.com/SJTU-ViSYS/Ground-Fusion),[LINK3d](https://github.com/YungeCui/LinK3D),[i-Octree](https://github.com/zhujun3753/i-octree),[LIO-EKF](https://github.com/YibinWu/LIO-EKF) and so on! | ||
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| ## Table of Contents | ||
| 1. [News & Updates](#news--updates) | ||
| 2. [Abstract](#abstract) | ||
| 3. [Main Contributions](#main-contributions) | ||
| 4. [Video](#video) | ||
| 5. [License](#license) | ||
| 6. [Sensor Setup](#sensor-setup) | ||
| - [Acquisition Platform](#acquisition-platform) | ||
| - [Sensor Parameters](#sensor-parameters) | ||
| 7. [Dataset Sequences](#dataset-sequences) | ||
| - [Outdoors](#outdoors) | ||
| - [Indoors](#indoors) | ||
| - [Alternative Indoors and Outdoors](#alternative-indoors-and-outdoors) | ||
| 8. [Configuration Files](#configuration-files) | ||
| 9. [Development Toolkits](#development-toolkits) | ||
| - [Extracting Images](#extracting-images) | ||
| - [Evaluation](#evaluation) | ||
| - [Calibration](#calibration) | ||
| - [Getting RINEX Files](#getting-rinex-files) | ||
| - [ROS Drivers for UVC Cameras](#ros-drivers-for-uvc-cameras) | ||
| 10. [Future Plans](#future-plans) | ||
| 11. [Acknowledgement](#acknowlegement) | ||
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| - **⭐️`2024/03/05`**: | ||
| A quantity of cutting-edge SLAM systems have been tested on M2DGR by lovely users. [Here](https://blog.csdn.net/zardforever123/article/details/129194673) are the configuration files for [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2),[ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3),[VINS-Mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono),[DM-VIO](https://github.com/lukasvst/dm-vio),[A-LOAM](https://github.com/HKUST-Aerial-Robotics/A-LOAM),[Lego-LOAM](https://github.com/RobustFieldAutonomyLab/LeGO-LOAM),[LIO-SAM](https://github.com/TixiaoShan/LIO-SAM),[LVI-SAM](https://github.com/TixiaoShan/LVI-SAM),[LINS](https://github.com/ChaoqinRobotics/LINS---LiDAR-inertial-SLAM),[FastLIO2](https://github.com/hku-mars/FAST_LIO),[Fast-LIVO](https://github.com/hku-mars/FAST-LIVO),[Faster-LIO](https://github.com/gaoxiang12/faster-lio) and [hdl_graph_slam](https://github.com/koide3/hdl_graph_slam). Welcome to test! If you have more configuration files, please contact me and I will post it on this website ~ | ||
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| - **`2024/02/22`**: | ||
| - **🚀 `2024/02/22`**: | ||
| New paper has been accepted by ICRA2024! The dataset is [**M2DGR-plus**](https://github.com/SJTU-ViSYS/M2DGR-plus). And the algorithm code is [**Ground-Fusion**](https://github.com/SJTU-ViSYS/Ground-Fusion). The preprint paper is [here](http://arxiv.org/abs/2402.14308). | ||
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| - **`2022/06/20`**: | ||
| - **⭐️`2022/06/20`**: | ||
| Thanks Jialin Liu (Fudan University) for his work to test LVI-SAM on M2DGR. [**Here**](https://github.com/electech6/LVI-SAM_detailed_comments) is the link of their modified LVI-SAM version. And the configuration files for LVI-SAM on M2DGR are given in [launch file](https://github.com/SJTU-ViSYS/M2DGR/blob/main/module_sam.launch),[camera file](https://github.com/SJTU-ViSYS/M2DGR/blob/main/my_params_camera.yaml) and [lidar file](https://github.com/SJTU-ViSYS/M2DGR/blob/main/my_params_lidar.yaml). Feel free to test the demo on your machine! | ||
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| </div> | ||
| <p align="center">>LVI-SAM on M2DGR</p> | ||
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| - **`2022/02/18`**: We have upload a brand new SLAM dataset with GNSS, vision and IMU information. Here is our link [**SJTU-GVI**](https://github.com/sjtuyinjie/SJTU_GVI). Different from M2DGR, new data is captured on a real car and it records GNSS raw measurements with a Ublox ZED-F9P device to facilitate GNSS-SLAM. Give us a star and folk the project if you like it. | ||
| - **⭐️`2022/02/18`**: We have upload a brand new SLAM dataset with GNSS, vision and IMU information. Here is our link [**SJTU-GVI**](https://github.com/sjtuyinjie/SJTU_GVI). Different from M2DGR, new data is captured on a real car and it records GNSS raw measurements with a Ublox ZED-F9P device to facilitate GNSS-SLAM. Give us a star and folk the project if you like it. | ||
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| - **`2022/02/01`**: Our work has been accepted by **ICRA2022** and **RA-L2021**! | ||
| - **📄 `2022/02/01`**: The paper has been accepted by both [**RA-L**](https://www.ieee-ras.org/publications/ra-l/) and [**ICRA 2022**](https://icra2022.org/). The paper is provided in [Arxiv version](https://arxiv.org/abs/2112.13659) and [IEEE RA-L version](https://ieeexplore.ieee.org/document/9664374). | ||
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| ## 1.LICENSE | ||
| This work is licensed under MIT license. International License and is provided for academic purpose. If you are interested in our project for commercial purposes, please contact us on [email protected] for further communication. | ||
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| If you face any problem when using this dataset, feel free to propose an issue. And if you find our dataset helpful in your research, simply give this project a star | ||
| . | ||
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| The paper has been accepted by both [RA-L](https://www.ieee-ras.org/publications/ra-l/) and [ICRA 2022](https://icra2022.org/). The paper is provided in [Arxiv version](https://arxiv.org/abs/2112.13659) and [IEEE RA-L version](https://ieeexplore.ieee.org/document/9664374).If you use M2DGR in an academic work, please cite: | ||
| If you face any problem when using this dataset, feel free to propose an issue. And if you find our dataset helpful in your research, simply give this project a star. If you use M2DGR in an academic work, please cite: | ||
| ~~~ | ||
| @article{yin2021m2dgr, | ||
| title={M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots}, | ||
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