# AI-IMU Dead-Reckoning

arXiv: https://arxiv.org/abs/1904.06064 · HTML: https://mlsanity.com/paper/1904.06064/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 27 of 27)

- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [CTIN: Robust Contextual Transformer Network for Inertial Navigation](https://mlsanity.com/paper/2112.02143/) (2021)
- [Adaptive Kalman-Informed Transformer](https://mlsanity.com/paper/2401.09987/) (2024)
- [EqNIO: Subequivariant Neural Inertial Odometry](https://mlsanity.com/paper/2408.06321/) (2024)
- [AirIMU: Learning Uncertainty Propagation for Inertial Odometry](https://mlsanity.com/paper/2310.04874/) (2023)
- [Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network](https://mlsanity.com/paper/2402.00366/) (2024)
- [Gravity-aligned Rotation Averaging with Circular Regression](https://mlsanity.com/paper/2410.12763/) (2024)
- [Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models](https://mlsanity.com/paper/2204.06776/) (2022)
- [Towards Accurate Ground Plane Normal Estimation from Ego-Motion](https://mlsanity.com/paper/2212.04224/) (2022)
- [Deep Inertial Navigation using Continuous Domain Adaptation and Optimal Transport](https://mlsanity.com/paper/2106.15178/) (2021)
- [NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoT](https://mlsanity.com/paper/2404.08939/) (2024)
- [Holistic Fusion: Task- and Setup-Agnostic Robot Localization and State Estimation with Factor Graphs](https://mlsanity.com/paper/2504.06479/) (2025)
- [PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms](https://mlsanity.com/paper/2601.03040/) (2026)
- [CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios](https://mlsanity.com/paper/2605.15122/) (2026)
- [Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network](https://mlsanity.com/paper/2303.11725/) (2023)
- [Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach](https://mlsanity.com/paper/2310.20024/) (2023)
- [Learning-Based Speed Estimation from Accelerometer-Only Inertial Sensing](https://mlsanity.com/paper/2401.07468/) (2024)
- [OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering](https://mlsanity.com/paper/2401.16719/) (2024)
- [An End-to-End Learning-Based Multi-Sensor Fusion for Autonomous Vehicle Localization](https://mlsanity.com/paper/2503.05088/) (2025)
- [Ninja Codes: Neurally Generated Fiducial Markers for Stealthy 6-DoF Tracking](https://mlsanity.com/paper/2510.18976/) (2025)
- [Feeling the Space: Egomotion-Aware Video Representation for Efficient and Accurate 3D Scene Understanding](https://mlsanity.com/paper/2603.17980/) (2026)
- [TESO: Online Tracking of Essential Matrix by Stochastic Optimization](https://mlsanity.com/paper/2604.19420/) (2026)
- [Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation](https://mlsanity.com/paper/2605.18704/) (2026)
- [Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors](https://mlsanity.com/paper/2607.05663/) (2026)
- [Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba](https://mlsanity.com/paper/2607.05669/) (2026)
- [GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry](https://mlsanity.com/paper/2609.25338/) (2026)

## Abstract (arXiv)

In this paper we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstructions, or for safe emergency stops in the extreme case of exteroceptive sensors failure. The key components of the method are the Kalman filter and the use of deep neural networks to dynamically adapt the noise parameters of the filter. The method is tested on the KITTI odometry dataset, and our dead-reckoning inertial method based only on the IMU accurately estimates 3D position, velocity, orientation of the vehicle and self-calibrates the IMU biases. We achieve on average a 1.10% translational error and the algorithm competes with top-ranked methods which, by contrast, use LiDAR or stereo vision. We make our implementation open-source at: https://github.com/mbrossar/ai-imu-dr