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https://github.com/SJTU-ViSYS/M2DGR-plus?tab=readme-ov-file

Extension and update of M2DGR: a novel Multi-modal and Multi-scenario SLAM Dataset for Ground Robots (ICRA2022 & ICRA2024)
https://github.com/SJTU-ViSYS/M2DGR-plus?tab=readme-ov-file

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Extension and update of M2DGR: a novel Multi-modal and Multi-scenario SLAM Dataset for Ground Robots (ICRA2022 & ICRA2024)

Host: GitHub
URL: https://github.com/SJTU-ViSYS/M2DGR-plus?tab=readme-ov-file
Owner: SJTU-ViSYS
Created: 2024-02-22T03:30:30.000Z (9 months ago)
Default Branch: main
Last Pushed: 2024-07-29T05:06:38.000Z (4 months ago)
Last Synced: 2024-07-29T06:33:02.623Z (4 months ago)
Homepage:
Size: 6.09 MB
Stars: 104
Watchers: 3
Forks: 5
Open Issues: 4
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

Awesome-3D-LiDAR-Datasets - M2DGR-plus

README

        # M2DGR-plus: Extension and update of [M2DGR](https://github.com/SJTU-ViSYS/M2DGR), a novel Multi-modal and Multi-scenario SLAM Dataset for Ground Robots (ICRA2022 & ICRA2024)

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

 

📝 [[Paper]](https://arxiv.org/abs/2402.14308)

 

➡️ [[Algorithm]](https://github.com/SJTU-ViSYS/Ground-Fusion)

 

🎯 [[M2DGR Dataset]](https://github.com/SJTU-ViSYS/M2DGR)

⭐️ [[Presentation Video]](https://www.bilibili.com/video/BV1xx421m75k/?spm_id_from=333.337.search-card.all.click&vd_source=0804300aea4065df90adde5398ee74b7)







Figure 1. Acquisition Platform and Diverse Scenarios.


## NOTICE

### This dataset is based on [M2DGR](https://github.com/SJTU-ViSYS/M2DGR). And the algorithm code is [Ground-Fusion](https://github.com/SJTU-ViSYS/Ground-Fusion). The preprint version of this paper is [arxiv](http://arxiv.org/abs/2402.14308).

## 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. 

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}

}

~~~

## 2.SENSOR SETUP

The calibration results are [here](https://github.com/SJTU-ViSYS/M2DGR-plus/blob/main/calibration.txt).

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

* **LIDAR** Robosense 16, 360 Horizontal Field of View (FOV),-30 to +10 vertical FOV,5Hz,Max Range 200 m,Range Resolution 3 cm, Horizontal Angular Resolution 0.2°.  

* **GNSS** Ublox F9p, GPS/BeiDou/Glonass/Galileo, 1Hz  

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

* **IMU**,wheeltec,9-axis,100Hz;  

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

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

The rostopics of our rosbag sequences are listed as follows:

* 3D LIDAR: `/rslidar_points` 

* 2D LIDAR: `/scan` 

* Odom: `/odom` 

* GNSS Ublox F9p:  

`/ublox_driver/ephem `,  

`/ublox_driver/glo_ephem `,

`/ublox_driver/range_meas `,

`/ublox_driver/receiver_lla `,

`/ublox_driver/receiver_pvt `

* V-I Sensor:  

`/camera/color/image_raw`,  

`/camera/imu`

* IMU: `/imu `

 

## 3.DATASET SEQUENCES

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

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

Anomaly|2023-8|1.5g|57s|wheel anomaly|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ef8corMuVwhJsWSpp-FXkREBrTduGBO8nifC9VEb5twHVg?e=CyEeMy)

Switch|2023-8|9.5g|292s|indoor-outdoor switch|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ESRoBZtYjrtAkOzZZZxjtLIBowQqF3G9Vz-jiaUCCy6E_A?e=RtZkwL)

Tree|2023-8|3.7g|160s|Dense tree leave cover|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EV9aZQbxo7pChOxZEWqdP0IBDpySkhtOXNIRKP3ijDK62Q?e=fW0afm)

Bridge_01|2022-11|2.4g|75s|Bridge, Zigzag|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EftDI1uQ_M1Hp4LZVof4sHgB4_IF2C9HBsWYZKAK2mr4EA?e=dydvKz)

Bridge_02|2022-11|16.0g|501s|Bridge, Long-term,Straight line|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUrTvD2zK2hNimekHiJS5rABME45O5s7ksSAJpd3ipD-BA?e=7aicGk)

Street_01|2022-11|1.7g|58s|Street, Straight line|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ebap2epwtTtHhWtp0AO_nnYB7S7zDZkkW-zTpYVmrHfOEA?e=JvDij7)

Street_02|2022-11|3.9g|126s|Bridge, Sharp turn|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EdqjNDDhVkJNhwA9DKlPnTsBIXh0xCGITpvQ1b4bG__k0A?e=chWjV8)

Parking_01|2022-11|3.3g|105s|Parking lot, Side moving|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EaCXXT2SAP9AmmqR1LUYwu4By3z5P3jhdeROv8EPdp9C0A?e=fQqJq5)

Parking_02|2022-11|5.4g|149s|Parking lot, Rectangle loop|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EX7UjX535NZBkaXSIX63Pg4BMDGXfIfkjS7JvL-0lUA8mQ?e=lAMTTu)

Building_01|2022-11|3.7g|120s|Building, Far features|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUqNPeUal1JDnSd9ZbYKo5EBoaQKrna5m23B7LxzAB-mtQ?e=QtWUal)

Building_02|2022-11|3.4g|110s|Building, Far features|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EQcO7OhqhYlCv1tAQJeD6EkBi50Ot3OPajKtZmZJlJFyUw?e=NBS9rs)

## 4. EXPERIMENTAL RESULTS

### We test methods with diverse senser settings to validate our benchmark dataset. Results shown that our dataset is a valid and effective testfield for localization methods.

And in some cases, our Ground-Fusion achieves comparable performance to Lidar SLAM!







Figure 2. The ATE RMSE (m) result on some sequences.








Figure 3. The visualized trajectory.


## 5. Configuration Files

We provide configuration files for several cutting-edge baseline methods, including [VINS-RGBD](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/vinsrgbd),[TartanVO](https://github.com/SJTU-ViSYS/Ground-Challenge/tree/main/config_files_gc/tartanvo),[VINS-Mono](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/vinsmono) and [VIW-Fusion](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/viwfusion) and 

[GVINS](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/gvins).

## Star History

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