# Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones

arXiv: https://arxiv.org/abs/1808.03485 · HTML: https://mlsanity.com/paper/1808.03485/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [IDOL: Inertial Deep Orientation-Estimation and Localization](https://mlsanity.com/paper/2102.04024/) (2021)
- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Learning Car Speed Using Inertial Sensors for Dead Reckoning Navigation](https://mlsanity.com/paper/2205.07883/) (2022)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer](https://mlsanity.com/paper/2109.03091/) (2021)
- [Support Vector Machine for Determining Euler Angles in an Inertial Navigation System](https://mlsanity.com/paper/2212.03550/) (2022)
- [Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments](https://mlsanity.com/paper/2605.07412/) (2026)

## Abstract (arXiv)

Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld smart-devices pose a problem for inertial odometry on these devices. We propose a scheme for constraining the inertial odometry problem by complementing non-linear state estimation by a CNN-based deep-learning model for inferring the momentary speed based on a window of IMU samples. We show the feasibility of the model using a wide range of data from an iPhone, and present proof-of-concept results for how the model can be combined with an inertial navigation system for three-dimensional inertial navigation.