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https://github.com/SJTU-ViSYS/M2DGR

M2DGR： a Multi-modal and Multi-scenario Dataset for Ground Robots(RA-L2021 & ICRA2022)
https://github.com/SJTU-ViSYS/M2DGR
dataset robotics slam
Last synced: 12 days ago
JSON representation
M2DGR： a Multi-modal and Multi-scenario Dataset for Ground Robots(RA-L2021 & ICRA2022)
Host: GitHub
URL: https://github.com/SJTU-ViSYS/M2DGR
Owner: SJTU-ViSYS
License: mit
Created: 2021-08-30T11:09:01.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-10-27T04:22:45.000Z (17 days ago)
Last Synced: 2024-10-27T05:25:07.279Z (17 days ago)
Topics: dataset, robotics, slam
Homepage:
Size: 8.34 MB
Stars: 888
Watchers: 18
Forks: 117
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

awesome-and-novel-works-in-slam - [Homepage
Awesome-3D-LiDAR-Datasets - M2DGR - 32C|No|Odom|Large| (Summary Table / Update: 2023-07-12)
README

        

# M2DGR： a Multi-modal and Multi-scenario SLAM Dataset for Ground Robots （RA-L & ICRA2022）



💎 First Author: [**Jie Yin 殷杰**](https://sjtuyinjie.github.io/)

 

📝 [[Paper]](https://arxiv.org/abs/2112.13659)

 

➡️ [[Dataset Extension]](https://github.com/SJTU-ViSYS/M2DGR-plus)

 

⭐️[[Presentation Video]](https://www.youtube.com/watch?v=73enWUwxJ1k)

 

🔥[[Article]](https://mp.weixin.qq.com/s/J6872xPeBHaxl39jbsHYxg)









Figure 1. Sample Images


## 🎯 Notice

We strongly recommend that the newly proposed SLAM algorithm be tested on our [M2DGR](https://github.com/SJTU-ViSYS/M2DGR) / [M2DGR-plus](https://github.com/SJTU-ViSYS/M2DGR-plus) / [Ground-Challenge](https://github.com/sjtuyinjie/Ground-Challenge) / [SJTU-GVI](https://github.com/sjtuyinjie/SJTU_GVI) benchmark, because our data has following features:

1. **Rich 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 cutting-edge 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 these SOTA systems on our benchmark, your paper will be much more convincing and valuable.

4. 🔥 Extensive excellent **open-source** projects have been built or evaluated on M2DGR/M2DGE-plus so far, for examples, [**Ground-Fusion**](https://github.com/SJTU-ViSYS/Ground-Fusion), [LVI-SAM-Easyused](https://github.com/Cc19245/LVI-SAM-Easyused), [MM-LINS](https://github.com/lian-yue0515/MM-LINS/), [Log-LIO](https://github.com/tiev-tongji/LOG-LIO), [LIGO](https://github.com/Joanna-HE/LIGO.), [Swarm-SLAM](https://github.com/MISTLab/Swarm-SLAM), [VoxelMap++](https://github.com/uestc-icsp/VoxelMapPlus_Public), [GRIL-Cali](https://github.com/Taeyoung96/GRIL-Calib), [LINK3d](https://github.com/YungeCui/LinK3D), [i-Octree](https://github.com/zhujun3753/i-octree), [LIO-EKF](https://github.com/YibinWu/LIO-EKF), [Fast-LIO ROS2](https://github.com/Lee-JaeWon/FAST_LIO_ROS2), [HC-LIO](https://github.com/piluohong/hc_lio), [LIO-RF](https://github.com/YJZLuckyBoy/liorf), [PIN-SLAM](https://github.com/PRBonn/PIN_SLAM), [LOG-LIO2](https://github.com/tiev-tongji/LOG-LIO2), [Section-LIO](https://github.com/mengkai98/Section-LIO), [I2EKF-LO](https://github.com/YWL0720/I2EKF-LO), [Liloc](https://github.com/Yixin-F/LiLoc), [BMBL](https://github.com/YixFeng/Block-Map-Based-Localization), [Light-LOAM](https://github.com/BrenYi/Light-LOAM) and so on!

## Table of Contents

1. 🚩 [**News & Updates**](#news--updates)

2. [Introduction](#introduction)

   - [Abstract](#abstract)

   - [Main Contributions](#main-contributions)

   - [Video](#video)

6. [License](#license)

7. [Sensor Setup](#sensor-setup)

    - [Acquisition Platform](#acquisition-platform)

    - [Sensor Parameters](#sensor-parameters)

8. ⭐️ [**Dataset Sequences**](#dataset-sequences)

    - [Outdoors](#outdoors)

    - [Indoors](#indoors)

    - [Alternative Indoors and Outdoors](#alternative-indoors-and-outdoors)

9. 📝 [**Configuration Files**](#configuration-files)

10. [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)

11. [Star History](#star-history)

11. [Acknowledgement](#acknowlegement)

> [!TIP]

> Check the table of contents above for a quick overview. And check the below news for lateset updates, especially the list of projects based on M2DGR.

## News & Updates

- **🔥`2024/10/11`**: Introducing **[M2DGR-benchmark](https://github.com/sjtuyinjie/M2DGR-Benchmark)**, benchmarking newest SOTA LiDAR-visual SLAM alrogithms on both M2DGR and M2DGR-plus!

- **`2024/07/15`**: Introducing a list of LiDAR-Visual SLAM systems at [awesome-LiDAR-Visual-SLAM](https://github.com/sjtuyinjie/awesome-LiDAR-Visual-SLAM), wheel-based SLAM systems at [awesome-wheel-slam](https://github.com/sjtuyinjie/awesome-wheel-slam), and Isaac Sim resources at [awesome-isaac-sim](https://github.com/sjtuyinjie/awesome-isaac-sim) (keep updating)

- **🔥`2024/05/16`**: Introducing a list of excellent projects based on M2DGR dataset: (keep updating)

   - **Dataset extension:** [Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases](https://github.com/SJTU-ViSYS/Ground-Fusion) (with M2DGR-Plus)from ICRA2024

   

   - **Calibration:** [GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method using Ground Plane Motion Constraints](https://github.com/Taeyoung96/GRIL-Calib) from RA-L2023

   

   

   - **Survey:** [Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned](https://arxiv.org/pdf/2304.04362) from IROS2023

   

   

   - **SLAM modules:**

      - [LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud](https://arxiv.org/pdf/2206.05927v3) from RA-L2024

      - [VoxelMap++: Mergeable Voxel Mapping Method for Online LiDAR(-inertial) Odometry](https://arxiv.org/pdf/2308.02799) from RA-L2023

   

   - **SLAM systems:**

      - [MSF-SLAM: Multi-Sensor-Fusion-Based Simultaneous Localization and Mapping for Complex Dynamic Environments](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10682980) from TITS2024

      - [Block-Map-Based Localization in Large-Scale Environment](https://arxiv.org/pdf/2404.18192) from ICRA2024

      - [A High-Precision LiDAR-Inertial Odometry via Invariant Extended Kalman Filtering and Efficient Surfel Mapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10484977) from TIM2024

      - [I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry](https://arxiv.org/pdf/2407.02190) from IROS2024

      

      - [mVLINS: A Multilevel Visual-LiDAR-Inertial Navigation System with Completely Decoupled Odometry and Adaptive Environmental Mapping](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10457028) from TIV2024

   

      - [Fast and Robust LiDAR-Inertial Odometry by Tightly-Coupled Iterated Kalman Smoother and Robocentric Voxels](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517905) from TITS2024

   

      - [LIVER: A Tightly Coupled LiDAR-Inertial-Visual State Estimator With High Robustness for Underground Environments](https://ieeexplore.ieee.org/abstract/document/10404014) from RA-L2024

   

      - [LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation](https://arxiv.org/abs/2307.09531) from RA-L2023

   

   

      - [Efficient 3D Deep LiDAR Odometry](https://ieeexplore.ieee.org/abstract/document/9893384?casa_token=nt4pH4Uf4VAAAAAA:MN3ToBfpuxiXf4Q4rHsviWamV3yfkyYhKmMztrQSdec_No80c4Hvtb3ZELI2ym__HXqqd2NM2KGm) from T-PAMI2022

   

      - [Swarm-SLAM: Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems](https://arxiv.org/pdf/2301.06230) from RA-L2023

      

    

      - [DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry](https://arxiv.org/pdf/2302.01703) from ICRA2023

- **⭐️`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 ~

- **🚀 `2024/02/22`**:

New paper has been accepted by ICRA2024! The dataset is [**M2DGR-plus**](https://github.com/SJTU-ViSYS/M2DGR-plus) and [**Ground-Challenge**](https://github.com/sjtuyinjie/Ground-Challenge). 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).

- **⭐️`2022/06/20`**:

Thanks Jialin Liu (Fudan University) for his work to test LVI-SAM on M2DGR. Check out [**Here**](https://github.com/electech6/LVI-SAM_detailed_comments) for a [**modified LVI-SAM version**] for M2DGR. 













>LVI-SAM on M2DGR


- **⭐️`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.

- **📄 `2022/02/01`**:   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).

> [!NOTE]

> If you build your open-source project based on M2DGR or test a cutting-edge SLAM system on M2DGR, please write a issue to remind me of updating your contributions.

## INTRODUCTION

### ABSTRACT:

We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Global Navigation Satellite System (GNSS) receiver and a GNSS-IMU navigation system with real-time kinematic (RTK) signals. All those sensors were well-calibrated and synchronized, and their data were recorded simultaneously. The ground truth trajectories were obtained by the motion capture device, a laser 3D tracker, and an RTK receiver.  The dataset comprises 36 sequences (about 1TB) captured in diverse scenarios including both indoor and outdoor environments. We evaluate state-of-the-art SLAM algorithms on M2DGR. Results show that existing solutions perform poorly in some scenarios. For the benefit of the research community, we make the dataset and tools public.

Keywords:Dataset, Multi-model, Multi-scenario,Ground Robot

### MAIN CONTRIBUTIONS:

* We collected long-term challenging sequences for ground robots both indoors and outdoors with a complete sensor suite, which includes six surround-view fish-eye cameras, a sky-pointing fish-eye camera, a perspective color camera, an event camera, an infrared camera, a 32-beam LIDAR, two GNSS receivers, and two IMUs. To our knowledge, this is the first SLAM dataset focusing on ground robot navigation with such rich sensory information.

* We recorded trajectories in a few challenging scenarios like lifts, complete darkness, which can easily fail existing localization solutions. These situations are commonly faced in ground robot applications, while they are seldom discussed in previous datasets.

* We launched a comprehensive benchmark for ground robot navigation. On this benchmark, we evaluated existing state-of-the-art SLAM algorithms of various designs and analyzed their characteristics and defects individually.

### VIDEO

[![ICRA2022 Presentation](cover.png)](https://www.youtube.com/watch?v=73enWUwxJ1k)

For Chinese users, try [![bilibili](cover.png)](https://www.bilibili.com/video/BV1q3411G7iF/)

## 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. 

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},

  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}

}

~~~

## SENSOR SETUP

### Acquisition Platform

Physical drawings and schematics of the ground robot is given below. The unit of the figures is centimeter.







Figure 2. The GAEA Ground Robot Equipped with a Full Sensor Suite.The directions of the sensors are marked in different colors,red for X,green for Y and blue for Z.


### Sensor parameters

All the sensors and track devices and their most important parameters are listed as below:

* **LIDAR** Velodyne VLP-32C, 360 Horizontal Field of View (FOV),-30 to +10 vertical FOV,10Hz,Max Range 200 m,Range Resolution 3 cm, Horizontal Angular Resolution 0.2°.  

* **RGB Camera** FLIR Pointgrey CM3-U3-13Y3C-CS,fish-eye lens,1280*1024,190 HFOV,190 V-FOV, 15 Hz  

* **GNSS** Ublox M8T, GPS/BeiDou, 1Hz  

* **Infrared Camera**,PLUG 617,640*512,90.2 H-FOV,70.6 V-FOV,25Hz;  

* **V-I Sensor**,Realsense d435i,RGB/Depth 640*480,69H-FOV,42.5V-FOV,15Hz;IMU 6-axix, 200Hz  

* **Event Camera** Inivation DVXplorer, 640*480,15Hz;  

* **IMU**,Handsfree A9,9-axis,150Hz;  

* **GNSS-IMU** Xsens Mti 680G. GNSS-RTK,localization precision 2cm,100Hz;IMU 9-axis,100 Hz;  

* **Laser Scanner** Leica MS60, localization 1mm+1.5ppm  

* **Motion-capture System** Vicon Vero 2.2, localization accuracy 1mm, 50 Hz;

The rostopics of our rosbag sequences are listed as follows:

* LIDAR: `/velodyne_points` 

* RGB Camera: 

`/camera/left/image_raw/compressed `,  

`/camera/right/image_raw/compressed `,  

`/camera/third/image_raw/compressed `,  

`/camera/fourth/image_raw/compressed `,  

`/camera/fifth/image_raw/compressed `,  

`/camera/sixth/image_raw/compressed `,  

`/camera/head/image_raw/compressed `  

* GNSS Ublox M8T:  

`/ublox/aidalm `,  

`/ublox/aideph `,  

`/ublox/fix `,  

`/ublox/fix_velocity `,  

`/ublox/monhw `,  

`/ublox/navclock `,  

`/ublox/navpvt `,  

`/ublox/navsat `,  

`/ublox/navstatus `,  

`/ublox/rxmraw `  

* Infrared Camera:`/thermal_image_raw ` 

* V-I Sensor:  

`/camera/color/image_raw/compressed `,  

`/camera/imu`

* Event Camera:  

`/dvs/events`,  

`/dvs_rendering/compressed`

* IMU: `/handsfree/imu `

 

## DATASET SEQUENCES

**We make public ALL THE SEQUENCES with their GT now.**







Figure 3. A sample video with fish-eye image(both forward-looking and sky-pointing),perspective image,thermal-infrared image,event image and lidar odometry


An overview of M2DGR is given in the table below:

Scenario|Street|Circle|Gate|Walk|Hall|Door|Lift|Room|Roomdark|TOTAL

--|:--|:--:|--:|--:|--:|--:|--:|--:|--:|--:

Number | 10|2|3|1|5|2|4|3|6|36

Size/GB|590.7|50.6| 65.9|21.5| 117.4 |46.0|112.1|45.3|171.1|1220.6

Duration/s |7958 | 478| 782 |291 |1226|588|1224|275|866|13688

Dist/m |7727.72| 618.03| 248.40|263.17|845.15|200.14|266.27|144.13|395.66|10708.67

Ground Truth  |RTK/INS |RTK/INS |RTK/INS |RTK/INS |Leica |Leica |Leica |Mocap|Mocap| ---

### Outdoors





Figure 4. Outdoor Sequences:all trajectories are mapped in different colors.

  

Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag|GT

--|:--|:--:|--:|--:|--:|--:

gate_01|2021-07-31|16.4g|172s|dark,around gate|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ET3mU1rvdTpEl8VYvC25q7YB5pmPQlwru0jBbQ9iu0oAMA?e=LrKUpJ)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EfipLVtlRChHvwklkDPylPgBSIQry0_JdfqH-6DaxWCaNA?e=idVsY4)

gate_02|2021-07-31|27.3g|327s|dark,loop back|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EY7fHSh4NnxBvemze1JS8TEBy5beLh_xlJ6mdi2IYmeY9w?e=xIcvDe)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ETaXuADPMrNKlFn6pI9aqeoBmcgHr86fIsvDBB6cSaTBLA?e=gzaHPW)

gate_03|2021-08-04|21.9g|283s|day|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUthdjvVIVdFmFxR82jzVqUBQTpsfvvb26pYtb0yj-_hlw?e=6MWwmJ)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ETj90pEu4PBDpYIoI0nWEAMBiWU68VXMXpU7MKwWnpdXxA?e=1KLdX3)

Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag|GT

--|:--|:--:|--:|--:|--:|--:

Circle_01|2021-08-03|23.3g|234s|Circle|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUVwex_LapBFrWV4ZtXocoYBE29aoQkPVdwWGhSFcioEtQ?e=YRRV9L)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EUlLxD27cGFArKGnpwkGrgEBr9FVGaSojLGVhJSNKaLLqQ?e=Bkptlj)

Circle_02|2021-08-07|27.3g|244s|Circle|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EeVG96IFCfxDlDLH8xefa3EBEMjCm4_8__V8JZ4ivMGoww?e=FVgjhB)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EfVe45nxzepBsZSFp-yzqRMBpg_PYdZVS1L3FOYqA8WdcA?e=5BcVrA)

Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag|GT

--|:--|:--:|--:|--:|--:|--:

street_01|2021-08-06|75.8g|1028s|street and buildings,night,zigzag,long-term|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EavjoipiTMRIjUvmodSGGsoB8rg0_pOkp6pqDScr8h4zvQ?e=OjtWkL)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ES2todVoF5VGmLo8WwKxcRYB4nyw6-zNR86I4BxAPnv_wg?e=ZtalWi)

street_02|2021-08-03|83.2g|1227s|day,long-term|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EQj5QBBHONpFj-hlvXOQBr0BM0NP9nhNuw-X9UtwOMMuNw?e=ZrxudN)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EfOmlQh5BvJAorfLiI7orFgBJRR94rEjU163lvWgLAlA2Q?e=Z7mAcd)

street_03|2021-08-06|21.3g|354s|night,back and fourth,full speed|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EQU95R6TOAZIkaoFuHJLU-kB9qJEIDeEsECB3Gjc9Nmx8A?e=J1AKwY)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EQ4l4FNo69dPjWco2Pk8Zy4BuPWTFUGQHOv2KdO4MbTv2g?e=ZZf9ok)

street_04|2021-08-03|48.7g|858s|night,around lawn,loop back|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ea72BxSXFYhDrp_FGNlJ2ukBx3CQSlv0Wah5nFUJtIntrw?e=4rwi7H)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EadWM6nH74hBusYGre63WRcB0fM_ynkFJu2ibpSWtr5P-w?e=K7FJzV)

street_05|2021-08-04|27.4g|469s|night,staight line|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUClV6vL2zhAicOwwO1WiroBK-fPzTu8K8NtMfgdMAxIqw?e=r50mNo)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EYrYgnp716xKo0-wcyVX2Z4BTnbbZuQrwYgaJIzREs923Q?e=gcGt7I)

street_06|2021-08-04|35.0g|494s|night,one turn|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EZ4HAXvNQXRCgRKSLpE3yX0BsM24PkXwAd-NopVc7ueNzA?e=oUw91h)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ETFW7vEEmp9JvJiVZef0wooBN9r7mt8jj1WmVdzzNiIp7A?e=67FHW4)

street_07|2021-08-06|77.2g|929s|dawn,zigzag,sharp turns|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EfScTXrKjAdGg1w9xZ-yZgIBWu9gIswakQToN9guMPatQQ?e=YvjAUg)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EefcYHlgA-RJiTl1jZl9r4ABr2YcrmTX7InDG_W6cHHrFQ?e=NQB203)

street_08|2021-08-06|31.2g|491s|night,loop back,zigzag|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EdgojePkM2ZNszS6JM80D90B-2q68wWQ1vZijzeaH-IQrw?e=iwVIiX)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/Ecv4IRMW5rBPn22pxW-xEYQBGinyVxrP6htbpS3HGnOpiA?e=TJXzJz)

street_09|2021-08-07|83.2g|907s|day,zigzag|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ee5hiGAdou5OvPI_xeOHBh4BtRntB8-5qLXqCuXhXNB-Yw?e=wEkptw)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EXb__m3Z0wdLkv_ybnXDOlUBVV41XM8_G1yHNO9oZKJE1w?e=xaZBGo)

street_010|2021-08-07|86.2g|910s|day,zigzag|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EfcpNeq8p-NLp7kCkaz0WugBf1c9TGtDbVBDy2QHF5rPvQ?e=lpnnAJ)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ETWkYfjfaP5PuMUJmbHsLS0Btby-9W7V9n4bfob9DGP-pw?e=iOGOBW)

walk_01|2021-08-04|21.5g|291s|day,back and fourth|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EZn2REI4E2BLurJXTaTDpYcBIMOoO3CKh9dsPbcMeMYTKg?e=R4cSKy)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EfNamQlwIeRGl0-gjktD1UEBMpXSWScVLL7VL1WUPiInhw?e=zmbJNp)



### Indoors





Figure 5. Lift Sequences:The robot hang around a hall on the first floor and then went to the second floor by lift.A laser scanner track the trajectory outside the lift

  

  

Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag|GT

--|:--|:--:|--:|--:|--:|--:

lift_01|2021-08-04|18.4g|225s|lift|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EZp-yCPxrxNBg5cM_aWualABVCNktGm29u0RA2UGVmyp2Q?e=BChvEN)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EQkO6B_IDPtItavRTjwedkoBQDUc6vV2jqtkOsmnWL7lhw?e=dDNVaE)

lift_02|2021-08-04|43.6g|488s|lift|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EfXP5V6Yi3tEvQL-Gbaq4QcBEVNxqacC-tltlzYnFnW7zQ?e=vgyEzS)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EX9iTyrQjWpKu9dI0Vx9uUkBAeLjUuU-PwUVT4yl1Tit1Q?e=toxiWg)

lift_03|2021-08-15|22.3g|252s|lift|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EWWG7OgCmTpIj_VZixYTkzsBew8ONoMrI13acbZ_8svV3g?e=1OVDYA)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EQgymOgmbiVDt8NPcb-HiYoBUKkfzJZeJnKZdOvAXMgWtA?e=mzM6kq)

lift_04|2021-08-15|27.8g|299s|lift|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ETrPaBIVaV1EtTVUG9effPIBK1LiJ3pGK93jAdhLZU_Pjg?e=ekVtWl)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EYgfjuI3fZ9AsjGH1jKwwqcBlXsaRaEZ9TS8Y1VHlTpyCw?e=0fMc5n)

  

  

  

Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag|GT

--|:--|:--:|--:|--:|--:|--:

hall_01|2021-08-01|29.1g|351s|randon walk|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EWQ2bcxWRgZLtK_eSIgnNmoB_ozAyXeEU_MmlVqPZeiB7Q?e=BKghlK)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EYxqt8qdK2hCgzW7qoQQWEIBvWgJAC7rotStNWxiEXDigg?e=lJabX7)

hall_02|2021-08-08|15.0g|128s|randon walk|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EaAEMKhvsgJCn0bSvlNOENkB-jVOOH4gxrxATSCbwPdUng?e=xCXoXE)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EWybRtZdpVhBtfPbB4PlvigBNXjgmp8PzHbcMBqUA2ivhA?e=WRoQN9)

hall_03|2021-08-08|20.5g|164s|randon walk|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EWyFq_niW4xIkk9pg2tLVfMBfT9JaC0ZUa0CogD6sND6Ew?e=ODUNxq)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EVL6pWtJ0yVNgDkZLYonkrMBcVAVN8pO2F5Kdh5tSh9fdg?e=nGDMba)

hall_04|2021-08-15|17.7g|181s|randon walk|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EV0oYa2MNi1HqmhvCM1pbboBAqQlij03bPifdyd_cqZUDA?e=P56O61)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EUrqpdjawd9HgF7aVQxYL_cBKIye_pFquj3tobdSnj_H2w?e=iAEG0u)

hall_05|2021-08-15|35.1g|402s|circle|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EQRFrzmO2BxFmeAZV_ifTpsBJjdIM7XjQAnmnuDdhE9-Vg?e=WDZwUt)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EfuK4fEcs0JEtXGNWjEVI9UB9qHZjd7Y4WAs9XLZgmA4hQ?e=zQrXDW)



Figure 6. Room Sequences:under a Motion-capture system with twelve cameras.

  

Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag|GT

--|:--|:--:|--:|--:|--:|--:

room_01|2021-07-30|14.0g|72s|room,bright|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EfG372xf9h9Dl0xjm5XcDgoB7JP0SsWJfAfpfO2CU-QOmw?e=XINjaC)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ER6DA9bugPZNphRXA-zT_8QB4Hj0r0564NXknXHrlWuEcg?e=SlLMZM)

room_02|2021-07-30|15.2g|75s|room,bright|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EaVK6tu2gs5NnOpAhhWrTPEBK_cpPGiq_1vDXET2GTCeNQ?e=2QpXCE)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ES-9QFWKEeBNqxyMv5dJe7EB5YxfTzi8v3x2Hf1aLObnAg?e=ifwPHL)

room_03|2021-07-30|26.1g|128s|room,bright|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EZfZZNphLARHl0H4zLbM_kABbwkgl5efzhVqUeia8T-adQ?e=aWHDbk)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EbcfH0djHMxCos8kfIH4i6wBBazfAOcFu6DLDSThzATf-w?e=W630hf)

room_dark_01|2021-07-30|20.2g|111s|room,dark|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EXx8PoEtySBCvzFbYnQrFIkBDnjodZJ97_EVvXeSHW3snw?e=ZVsp9L)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/Ee4o6_FIKeNPqG-f2ahm7p0BPUExaiMABzHfvhj1xGRitQ?e=6IwX76)

room_dark_02|2021-07-30|30.3g|165s|room,dark|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ERbnfghuh-NFo9W0Sev8cggBQQiTQLzjFiQy5So7j3J9tw?e=KEbKXr)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/Ef_CAOf_e-VLlu9xdAm2QSIBTP0B1QNm7uRG-s9EWib4hg?e=95qPDA)

room_dark_03|2021-07-30|22.7g|116s|room,dark|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EXwiG7vYnsdKh2OotVeQKYsByEtckw39FPXiWLXBrA5kqw?e=0ZR9jg)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EXjFPcy4SrlCk5ZRvY9nGMkBzZuaSC6NVqPweU-1_OozuQ?e=E9uKVh)

room_dark_04|2021-08-15|29.3g|143s|room,dark|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EQqOn3hvPFVHin6apSr233wBJuG69N2iDPSauYQhKker4A?e=QUABfA)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EZ01sVBMNV9FloNLhb7XdeIB1bIp0GtwoxS0pXirZ9dO6A?e=KFFArd)

room_dark_05|2021-08-15|33.0g|159s|room,dark|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ead7Ayxgh0REuN1J5SPDFFUBKMF8X3w2eGBCjOgefOs-VA?e=czasTj)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ET5ihyrgN3FCpR_i6yZeVPYBBANJ57Cq5ly44ddTOpPl7Q?e=la7ifj)

room_dark_06|2021-08-15|35.6g|172s|room,dark|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUefMvN3g6hMos_qxocEH-4BYMFnt-yF4HxECfx3nDy81Q?e=laORp2)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/ESjB6YrPnKhMuSb-hM_ZSw0BltnaLhn41H6LgexZjebNsA?e=xuEjTp)



  

  

### Alternative indoors and outdoors

  





Figure 7. Door Sequences:A laser scanner track the robot through a door from indoors to outdoors.


Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag|GT

--|:--|:--:|--:|--:|--:|--:

door_01|2021-08-04|35.5g|461s|outdoor to indoor to outdoor,long-term|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ERxIk8o_HwlAgbqJ2wwgHl8Br3uBZhyuBbxM2bG_0A6QYA?e=uQr94R)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EVgYKmnhIy9AhCC1tOOKUOYB91dEx3mGmFQ9ewLtlNDw6g?e=uylMro)

door_02|2021-08-04|10.5g|127s|outdoor to indoor,short-term|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EWCKNoEfAmxGsahwnJDYWS4BfgEPoONQKR8HAuZA4ng5eg?e=KB9PYF)|[GT](https://sjtueducn-my.sharepoint.com/:t:/g/personal/594666_sjtu_edu_cn/EdOFufA_mTxCoVlzcu285xoBpNQkaTaYdfL8Nd0Nad6mBQ?e=bxEbOK)



## CONFIGURATION FILES

For convenience of evaluation, we provide configuration files of some well-known SLAM systems as below:

[A-LOAM](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/aloam/aloam_velodyne_HDL_32.launch), [LeGO-LOAM](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/legoloam/run.launch), [LINS](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/lins/exp_port.yaml), [LIO-SAM](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/liosam/params.yaml), [VINS-MONO](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/vins/mytest.launch), [ORB-Pinhole](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/orb3/paperd435i.yaml), [ORB-Fisheye](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/orb3/paperleft.yaml), [ORB-Thermal](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/orb3/paperthermal.yaml), and [CUBMAPSLAM](https://github.com/sjtuyinjie/toolkit/blob/main/config_files/cubemapslam/runCubemapstreet_06.sh).

Furthermore, 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 ~

## DEVELOPEMENT TOOLKIT

### Extracting Images

* For rosbag users, first make image view

~~~

roscd image_view

rosmake image_view

sudo apt-get install mjpegtools

~~~

open a terminal,type roscore.And then open another,type

~~~

rosrun image_transport republish compressed in:=/camera/color/image_raw raw out:=/camera/color/image_raw respawn="true"

~~~

* For non-rosbag users,just take advantage of following script  [export_tum](https://github.com/sjtuyinjie/toolkit/blob/main/export_tum.py),[export_euroc](https://github.com/sjtuyinjie/toolkit/blob/main/export_euroc.py) and [get_csv](https://github.com/sjtuyinjie/toolkit/blob/main/img2csv.py) to get data in formats of Tum or EuRoC.

### Evaluation

We use open-source tool [evo](https://github.com/MichaelGrupp/evo) for evalutation.

To install evo,type

~~~

pip install evo --upgrade --no-binary evo

~~~

To evaluate monocular visual SLAM,type

~~~

evo_ape tum street_07.txt your_result.txt -vaps

~~~

To evaluate LIDAR SLAM,type

~~~

evo_ape tum street_07.txt your_result.txt -vap

~~~

To test GNSS based methods,type

~~~

evo_ape tum street_07.txt your_result.txt -vp

~~~

### Calibration

For camera intrinsics,visit [Ocamcalib](http://sites.google.com/site/scarabotix/ocamcalib-toolbox) for omnidirectional model.

visit [Vins-Fusion](https://github.com/HKUST-Aerial-Robotics/VINS-Fusion) for pinhole and MEI model.

use [Opencv](https://opencv.org/) for Kannala Brandt model

For IMU intrinsics,visit [Imu_utils](https://github.com/gaowenliang/imu_utils)

For extrinsics between cameras and IMU,visit [Kalibr](https://github.com/ethz-asl/kalibr)

For extrinsics between Lidar and IMU,visit [Lidar_IMU_Calib](https://github.com/APRIL-ZJU/lidar_IMU_calib) 

For extrinsics between cameras and Lidar, visit [Autoware](https://github.com/Autoware-AI/autoware.ai) 

### Getting RINEX files

For GNSS based methods like [RTKLIB](http://www.rtklib.com/), we usually need to get data in the format of RINEX. To make use of GNSS raw measurements, we use [Link](https://github.com/TakahashiJinxu/ublox2rinex) toolkit.

### ROS drivers for UVC cameras 

We write a ROS driver for UVC cameras to record our thermal-infrared image. 

[UVC ROS driver](https://github.com/sjtuyinjie/toolkit/tree/main/thermal_ws/src)

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=SJTU-ViSYS/M2DGR&type=Timeline)](https://star-history.com/#Ashutosh00710/github-readme-activity-graph&Timeline)

## ACKNOWLEGEMENT

This work is supported by NSFC(62073214). Authors from SJTU hereby express our appreciation.