Open-IO/mipo-go1-odometry

MIPO (Unitree Go1) Multi-IMU Inertial Odometry Dataset

Paper | Project Page | Code

Indoor and outdoor recordings of a Unitree Go1 quadruped: the body IMU, four foot-mounted IMUs, all 12 joint states with 4 foot-force channels, and a position reference for each split (OptiTrack motion capture GT indoors, a phone-based reference outdoors, see below). It is the quadruped benchmark of SSIO, which trains inertial odometry from the robot's own unlabeled IMU stream.

The recordings are from Cerberus 2.0 and the paper Multi-IMU Proprioceptive Odometry for Legged Robots (Yang et al., IROS 2023), hence the shorthand MIPO. This release converts the original ROS bags to HDF5, one file per bag, and adds the calibration SSIO uses.

Dataset Structure

indoor/
├── mipo_time_offsets.json
├── mipo_joint_stats.json
├── 20230615_risqh_gait_analysis/*.h5
├── 20230620_risqh_robustness_test/*.h5
├── 20230625_gait/*.h5
└── 230622_robustness/*.h5
outdoor/
├── ahrs_gps_yaw_offsets.json
├── <place>/*.h5                 (5 places: schedley park, mill19 trail,
│                                 st mary catholic cemetery, frick park, CMU garage)
└── pgo_densified_ref/<place>/*.npz

DATA_README.md describes the robot sensor data shared by both splits (schema, units, rates) in full detail. Per-split specifics, ground truth, and calibration are in indoor/README.md and outdoor/README.md.

Indoor has OptiTrack motion-capture ground truth. Outdoor has no motion capture; its position reference comes from a phone carried near the robot by the operator, not mounted on it. Use it as a coarse position and visual-comparison reference, not for velocity, not for high-accuracy position, and not as a substitute for true ground truth as network input.

Outdoor split, St Mary: SSIO (red) against the phone GPS reference (blue).

Indoor Train/Valid/Test Split

Split	Sequences	Duration (min)
train	36	28.9
valid	12	6.7
test	12	10.0
Total in pools	60	45.6
Total indoor files (all)	79	57.7

The splits are the SSIO paper's, disjoint, and reflect what the SSIO loader configs actually use. The 19 indoor files outside the splits are kept in this release for completeness; 5 have motion-capture freezes and jumps, so their ground truth is unusable, and 1 has frozen IMU data at the end of the sequence (see indoor/README.md for details on these 6).

Quick Usage

pip install huggingface_hub
hf download Open-IO/mipo-go1-odometry --repo-type dataset --local-dir mipo_hdf5

Unlike a plain NumPy dataset, the HDF5 files here are intentionally unsynchronized and uncalibrated: no per-topic resampling, no kalibr time-offset or extrinsic applied, no phone-to-robot clock correction. Reading the groups directly with h5py will silently skip all of that and produce a result that looks plausible but is misaligned. It is recommended to use, or faithfully reapply, the calibration from the SSIO loader (Sequence.MIPO.MIPO_Sequence, the MIPO_* dataset configs), which already implements it correctly. Download into $DATA_ROOT/mipo_hdf5 and point the configs there.

License and Attribution

The recordings come from Cerberus 2.0 (AGPL-3.0) by Shuo Yang, Zixin Zhang, Benjamin Bokser, and Zachary Manchester. Please also cite their work:

@inproceedings{yang2023mipo,
      title={Multi-IMU Proprioceptive Odometry for Legged Robots},
      author={Yang, Shuo and Zhang, Zixin and Bokser, Benjamin and Manchester, Zachary},
      booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
      year={2023},
}

Citation

https://ssio-imu.github.io

@misc{qiu2026ssioselfsupervisedinertialodometry,
      title={SSIO: Self-Supervised Inertial Odometry},
      author={Yuheng Qiu and Yutian Chen and Carlton Cort and Weison Huang and Shibo Zhao and Pieter Abbeel and Rocky Duan and Koushil Sreenath and C. Karen Liu and Carmelo Sferrazza and Junyi Geng and Sebastian Scherer and Guanya Shi},
      year={2026},
      eprint={2610.25988},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2610.25988},
}