# Deep Inertial Odometry with Accurate IMU Preintegration

arXiv: https://arxiv.org/abs/2101.07061 · HTML: https://mlsanity.com/paper/2101.07061/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [Learning Longer-term Dependencies in RNNs with Auxiliary Losses](https://mlsanity.com/paper/1803.00144/) (2018)
- [Visual Odometry Revisited: What Should Be Learnt?](https://mlsanity.com/paper/1909.09803/) (2019)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [Do RNN and LSTM have Long Memory?](https://mlsanity.com/paper/2006.03860/) (2020)
- [OxIOD: The Dataset for Deep Inertial Odometry](https://mlsanity.com/paper/1809.07491/) (2018)
- [Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference](https://mlsanity.com/paper/2001.04061/) (2020)
- [DPC-Net: Deep Pose Correction for Visual Localization](https://mlsanity.com/paper/1709.03128/) (2017)
- [Accurate IMU Preintegration Using Switched Linear Systems For Autonomous Systems](https://mlsanity.com/paper/1907.08434/) (2019)

## Built on by (most cited first, 1 of 1)

- [RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements](https://mlsanity.com/paper/2303.01641/) (2023)

## Abstract (arXiv)

Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors. They are widely adopted in various autonomous systems. Motivated by the limitations in processing the noisy measurements from these sensors using their mathematical models, researchers have recently proposed various deep learning architectures to estimate inertial odometry in an end-to-end manner. Nevertheless, the high-frequency and redundant measurements from IMUs lead to long raw sequences to be processed. In this study, we aim to investigate the efficacy of accurate preintegration as a more realistic solution to the IMU motion model for deep inertial odometry (DIO) and the resultant DIO is a fusion of model-driven and data-driven approaches. The accurate IMU preintegration has the potential to outperform numerical approximation of the continuous IMU model used in the existing DIOs. Experimental results validate the proposed DIO.