One Unified Model
One submission must handle car, drone, quadruped, and handheld trajectories at once.
IROS 2026 Competition · Cross-Platform Inertial Positioning
IMU Odometry Challenge One model. Four embodiments. Raw IMU only.
Estimate 3D body-frame velocity from raw 6-axis IMU with one unified model across car, drone, quadruped, and handheld motion.
Challenge design
One benchmark across four motion domains.
Training and validation data include platform labels. The anonymized test set does not, requiring a single model to generalize across radically different dynamics without switching between platform-specific experts.
- Platforms
- 4
- Windows
- ~136K
- Total Duration
- 37.9 hrs
- Sample Rate
- 200 Hz
Platform-Blind Testing
Test labels and platform-revealing trajectory identifiers remain hidden from participants.
Trajectory-Level Evaluation
Predicted velocities are integrated, aligned with SE(3) Umeyama, and scored equally across platforms.
Open TartanIMU Baseline
Reference checkpoints, training code, and a browser demo provide a reproducible starting point.
Benchmark coverage
Four embodiments with distinct motion dynamics.
Each platform page provides split statistics, trajectory previews, sensor characteristics, and domain-specific evaluation context.
Protocol at a glance
From raw inertial windows to ranked trajectories.
The model interface stays compact while evaluation measures the accumulated motion that matters in deployment.
01
The Task
- Input
- Raw 6-axis IMU: acceleration in m/s² and angular velocity in rad/s, sampled at 200 Hz.
- Context
- One-second windows of 200 samples, provided as sequences of 10 consecutive windows.
- Output
- Per-window 3D body-frame velocity v = (vx, vy, vz) in m/s.
02
Scoring
- Primary
- Macro-averaged Absolute Trajectory Error (ATE), with all four platforms weighted equally.
- Evaluation
- Organizers integrate predicted velocities with ground-truth orientation, then align and score each path.
- Splits
- Train for fitting, validation for selection, and anonymized public/private test trajectories for ranking.
Organizers and partners
Built by teams working on robust robot perception.
The challenge is organized by CMU AirLab and Amazon FAR in conjunction with the IROS 2026 workshop on interoceptive perception.
amazonFAR
Announcements
Challenge updates.
Latest
Final Kaggle submissions, the technical report, and model weights are due September 20, 2026, at 23:55 UTC. See the rules timeline.
The competition is live on Kaggle: TartanIMU Challenge: Multi-Platform Inertial Odometry.
The official dataset release hub is available from the challenge data page.
Platform pages for Car, Drone, Quadruped, and Handheld are published.
The initial IMU Odometry Challenge website and benchmark structure launched.
Research resources
Citation and reference models.
Run the TartanIMU specialist models in the Hugging Face demo. Cite the paper and benchmark when using these resources.
TartanIMU (CVPR 2025)
@InProceedings{Zhao_2025_CVPR,
author = {Zhao, Shibo and Zhou, Sifan and Blanchard,
Raphael and Qiu, Yuheng and Wang, Wenshan and
Scherer, Sebastian},
title = {Tartan IMU: A Light Foundation Model for
Inertial Positioning in Robotics},
booktitle = {Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2025},
pages = {22520-22529}
}IMU Odometry Challenge
@misc{imuchallenge2026,
title={IMU Odometry Challenge: Cross-Platform Inertial
Positioning Benchmark},
author={CMU AirLab and Super Odometry Group},
year={2026},
howpublished={\url{https://superodometry.com/imuchallenge/}},
note={Dataset and benchmark challenge page}
}