mlsanityData to 14 Oct 2026

Learning IMU Bias with Diffusion Model

arXiv 2505.11763 ↗First posted 17 May 2025cs.ROcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Motion sensing and tracking with IMU data is essential for spatial intelligence, which however is challenging due to the presence of time-varying stochastic bias. IMU bias is affected by various factors such as temperature and vibration, making it highly complex and difficult to model analytically. Recent data-driven approaches using deep learning have shown promise in predicting bias from IMU readings. However, these methods often treat the task as a regression problem, overlooking the stochatic nature of bias. In contrast, we model bias, conditioned on IMU readings, as a probabilistic distribution and design a conditional diffusion model to approximate this distribution. Through this approach, we achieve improved performance and make predictions that align more closely with the known behavior of bias.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 28 of its 40 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

28 of 40 references.

Closest ancestors · 2

Same direction, newest first

  1. 2020Denoising Diffusion Implicit Models6 Oct
  2. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  5. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  6. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  7. 2017VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimatorcited by 423
  8. 2022Stochastic Trajectory Prediction via Motion Indeterminacy Diffusioncited by 185
  9. 2019RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methodscited by 73
  10. 2020The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truthcited by 61
  11. 2018IONet: Learning to Cure the Curse of Drift in Inertial Odometrycited by 55
  12. 2020TLIO: Tight Learned Inertial Odometrycited by 55
14 more
  1. 2017RIDI: Robust IMU Double Integrationcited by 41
  2. 2019Visual-Inertial Navigation: A Concise Reviewcited by 28
  3. 2020Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimationcited by 26
  4. 2021IDOL: Inertial Deep Orientation-Estimation and Localizationcited by 26
  5. 2023Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directionscited by 21
  6. 2021RIO: Rotation-equivariance supervised learning of robust inertial odometrycited by 14
  7. 2022Neural Inertial Localizationcited by 13
  8. 2022Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphscited by 13
  9. 2022Learned Inertial Odometry for Autonomous Drone Racingcited by 12
  10. 2018Observability Analysis of Aided INS with Heterogeneous Features of Points, Lines and Planescited by 10
  11. 2021IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approachcited by 8
  12. 2024EqNIO: Subequivariant Neural Inertial Odometrycited by 8
  13. 2017Inertial Odometry on Handheld Smartphonescited by 7
  14. 2023AirIMU: Learning Uncertainty Propagation for Inertial Odometrycited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2026Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategiescited by 9
  2. 2025Towards Generalizable Human Activity Recognition: A Surveycited by 6
  3. 2025Diffusion Denoiser-Aided Gyrocompassingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Refining Diffusion Models for Motion Synthesis with an Acceleration Loss to Generate Realistic IMU Data
  2. 2026Overcoming the Intrinsic Performance Limitations of MEMS IMU via Diffusion-Based Generative Learning
  3. 2023Unmasking Bias in Diffusion Model Training
  4. 2024Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations
  5. 2025DnD Filter: Differentiable State Estimation for Dynamic Systems using Diffusion Models
  6. 2023Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps
  7. 2025Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
  8. 2025An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models