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

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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]

Author Paper Preprint Dataset License Video News

Figure 1. Acquisition Platform and Diverse Scenarios.

News & Updates

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 [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}
}

@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

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,TartanVO,VINS-Mono and VIW-Fusion and GVINS.

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