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

First Author: Jie Yin 殷杰 📝 [Paper] / [Arxiv] 🎯 [M2DGR Dataset] ⭐️ [Presentation Video] 🔥[News]

</div> <div align=center> <img src="./fig/car2.jpg" width="800px"> </div> <p align="center">Figure 1. Acquisition Platform and Diverse Scenarios.</p>

News & Updates

🔥2024/10/11: Introducing 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, wheel-based SLAM systems at awesome-wheel-slam, and Isaac Sim resources at awesome-isaac-sim (keep updating)

This dataset is based on M2DGR. And the algorithm code is Ground-Fusion. The preprint version of this paper is arxiv.

1.LICENSE

This work is licensed under GPL-3.0 license. International License and is provided for academic purpose. If you are interested in our project for commercial purposes, please contact us on robot_yinjie@outlook.com 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}
}

@INPROCEEDINGS{yin2024ground,
  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)}, 
  title={Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases}, 
  year={2024},
  volume={},
  number={},
  pages={8603-8609},
  keywords={Location awareness;Visualization;Simultaneous localization and mapping;Accuracy;Wheels;Sensor fusion;Land vehicles},
  doi={10.1109/ICRA57147.2024.10610070}}

2.SENSOR SETUP

The calibration results are here. 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
Switch	2023-8	9.5g	292s	indoor-outdoor switch	Rosbag
Tree	2023-8	3.7g	160s	Dense tree leave cover	Rosbag
Bridge_01	2022-11	2.4g	75s	Bridge, Zigzag	Rosbag
Bridge_02	2022-11	16.0g	501s	Bridge, Long-term,Straight line	Rosbag
Street_01	2022-11	1.7g	58s	Street, Straight line	Rosbag
Street_02	2022-11	3.9g	126s	Bridge, Sharp turn	Rosbag
Parking_01	2022-11	3.3g	105s	Parking lot, Side moving	Rosbag
Parking_02	2022-11	5.4g	149s	Parking lot, Rectangle loop	Rosbag
Building_01	2022-11	3.7g	120s	Building, Far features	Rosbag
Building_02	2022-11	3.4g	110s	Building, Far features	Rosbag

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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!

<div align=center> <img src="./fig/resultf.png" width="800px"> </div> <p align="center">Figure 2. The ATE RMSE (m) result on some sequences.</p> <div align=center> <img src="./fig/result.png" width="800px"> </div> <p align="center">Figure 3. The visualized trajectory.</p>

5. Configuration Files

We provide configuration files for several cutting-edge baseline methods, including VINS-RGBD,TartanVO,VINS-Mono and VIW-Fusion and GVINS.