A GNSS-Visual-IMU benchmark Dataset for SLAM
This is part of the dataset for tests in paper M2C-GVIO Different from M2DGR, this dataset has following features:
1.More light-weight and easy for downloading.
2.Recoreded on a real car with high speed.
3.GNSS raw measurements are captured by a Ublox ZED-F9P receiver, which facilitate the GNSS-SLAM research.
Please give us a star if this project is helpful to your research. Thank you! If you use M2DGR in an academic work, please cite:
@ARTICLE{9664374,
author={Yin, Jie and Li, Ang and Li, Tao and Yu, Wenxian and Zou, Danping},
journal={IEEE Robotics and Automation Letters},
title={M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/LRA.2021.3138527}}
@article{hua2023m2c,
title={M2C-GVIO: motion manifold constraint aided GNSS-visual-inertial odometry for ground vehicles},
author={Hua, Tong and Pei, Ling and Li, Tao and Yin, Jie and Liu, Guoqing and Yu, Wenxian},
journal={Satellite Navigation},
volume={4},
number={1},
pages={1--15},
year={2023},
publisher={SpringerOpen}
}
Figure 1. A picture of our acquisition platform.
Figure 2. We were calibrating the extrinsics.
The extraction code is "yj66"
| Sequence | Collection Date | Total Size | Duration | Rosbag |
|---|---|---|---|---|
| Seq1 | 2021-12-23 | 2.37G | 349s | Rosbag |
| Seq2 | 2021-12-23 | 921M | 109s | Rosbag |
| Seq3 | 2021-12-23 | 1.19G | 146s | Rosbag |
| Seq4 | 2021-12-23 | 850M | 112s | Rosbag |
| Seq5 | 2021-12-23 | 1.26G | 159s | Rosbag |
The camera intrinsic and cam-imu extrinsics calibration results is given in Link
The ground truth of the datasets is given in Link
Authors express our appreciation for the support of Shanghai West Hongqiao Navigation Technology Co., LTD.