# Using Inertial Sensors for Position and Orientation Estimation

arXiv: https://arxiv.org/abs/1704.06053 · HTML: https://mlsanity.com/paper/1704.06053/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 18 of 18)

- [CTIN: Robust Contextual Transformer Network for Inertial Navigation](https://mlsanity.com/paper/2112.02143/) (2021)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [STARNet: Sensor Trustworthiness and Anomaly Recognition via Approximated Likelihood Regret for Robust Edge Autonomy](https://mlsanity.com/paper/2309.11006/) (2023)
- [Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions](https://mlsanity.com/paper/2210.09168/) (2022)
- [Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee](https://mlsanity.com/paper/2103.02357/) (2021)
- [Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems](https://mlsanity.com/paper/2202.02247/) (2022)
- [Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field](https://mlsanity.com/paper/2203.15866/) (2022)
- [Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies](https://mlsanity.com/paper/2205.10778/) (2022)
- [Dynamic Sensor Matching based on Geomagnetic Inertial Navigation](https://mlsanity.com/paper/2208.06233/) (2022)
- [RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements](https://mlsanity.com/paper/2303.01641/) (2023)
- [Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements](https://mlsanity.com/paper/2102.12418/) (2021)
- [Learning deep autoregressive models for hierarchical data](https://mlsanity.com/paper/2104.13853/) (2021)
- [Using Probabilistic Movement Primitives in Analyzing Human Motion Difference under Transcranial Current Stimulation](https://mlsanity.com/paper/2107.02063/) (2021)
- [KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study](https://mlsanity.com/paper/2301.03469/) (2023)
- [Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods](https://mlsanity.com/paper/2304.06548/) (2023)
- [4D-based Robot Navigation Using Relativistic Image Processing](https://mlsanity.com/paper/2410.22087/) (2024)
- [Seeing My Future: Predicting Situated Interaction Behavior in Virtual Reality](https://mlsanity.com/paper/2510.10742/) (2025)
- [An Opticalmechanics Framework for Dynamic Estimation of Multibody Systems](https://mlsanity.com/paper/2606.09383/) (2026)

## Abstract (arXiv)

In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.