# AirIMU: Learning Uncertainty Propagation for Inertial Odometry

arXiv: https://arxiv.org/abs/2310.04874 · HTML: https://mlsanity.com/paper/2310.04874/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [TLIO: Tight Learned Inertial Odometry](https://mlsanity.com/paper/2007.01867/) (2020)
- [RIDI: Robust IMU Double Integration](https://mlsanity.com/paper/1712.09004/) (2017)
- [PyPose: A Library for Robot Learning with Physics-based Optimization](https://mlsanity.com/paper/2209.15428/) (2022)
- [AI-IMU Dead-Reckoning](https://mlsanity.com/paper/1904.06064/) (2019)
- [Multivariate Uncertainty in Deep Learning](https://mlsanity.com/paper/1910.14215/) (2019)
- [Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation](https://mlsanity.com/paper/2002.10718/) (2020)
- [IDOL: Inertial Deep Orientation-Estimation and Localization](https://mlsanity.com/paper/2102.04024/) (2021)
- [Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments](https://mlsanity.com/paper/2104.14938/) (2021)
- [Learning Inertial Odometry for Dynamic Legged Robot State Estimation](https://mlsanity.com/paper/2111.00789/) (2021)
- [RIO: Rotation-equivariance supervised learning of robust inertial odometry](https://mlsanity.com/paper/2111.11676/) (2021)
- [Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphs](https://mlsanity.com/paper/2211.04517/) (2022)
- [LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking](https://mlsanity.com/paper/2008.07196/) (2020)
- [ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization](https://mlsanity.com/paper/2207.12317/) (2022)
- [IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach](https://mlsanity.com/paper/2103.14286/) (2021)
- [Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation](https://mlsanity.com/paper/2007.14759/) (2020)
- [VIO-DualProNet: Visual-Inertial Odometry with Learning Based Process Noise Covariance](https://mlsanity.com/paper/2308.11228/) (2023)
- [Associating Uncertainty to Extended Poses for on Lie Group IMU Preintegration with Rotating Earth](https://mlsanity.com/paper/2007.14097/) (2020)

## Built on by (most cited first, 6 of 6)

- [MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry](https://mlsanity.com/paper/2409.09479/) (2024)
- [Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy](https://mlsanity.com/paper/2406.16087/) (2024)
- [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025)
- [Learning IMU Bias with Diffusion Model](https://mlsanity.com/paper/2505.11763/) (2025)
- [A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry](https://mlsanity.com/paper/2503.12527/) (2025)
- [Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments](https://mlsanity.com/paper/2605.07412/) (2026)

## Abstract (arXiv)

Inertial odometry (IO) using strap-down inertial measurement units (IMUs) is critical in many robotic applications where precise orientation and position tracking are essential. Prior kinematic motion model-based IO methods often use a simplified linearized IMU noise model and thus usually encounter difficulties in modeling non-deterministic errors arising from environmental disturbances and mechanical defects. In contrast, data-driven IO methods struggle to accurately model the sensor motions, often leading to generalizability and interoperability issues. To address these challenges, we present AirIMU, a hybrid approach to estimate the uncertainty, especially the non-deterministic errors, by data-driven methods and increase the generalization abilities using model-based methods. We demonstrate the adaptability of AirIMU using a full spectrum of IMUs, from low-cost automotive grades to high-end navigation grades. We also validate its effectiveness on various platforms, including hand-held devices, vehicles, and a helicopter that covers a trajectory of 262 kilometers. In the ablation study, we validate the effectiveness of our learned uncertainty in an IMU-GPS pose graph optimization experiment, achieving a 31.6% improvement in accuracy. Experiments demonstrate that jointly training the IMU noise correction and uncertainty estimation synergistically benefits both tasks.