Inertial Odometry on Handheld Smartphones

TL;DR

This paper demonstrates that with careful probabilistic modeling and sensor bias correction, smartphones can perform real-time inertial odometry for indoor navigation, despite the limited quality of built-in sensors.

Contribution

It introduces a probabilistic inertial odometry method that online learns IMU biases and fuses multiple sensor updates, enabling accurate smartphone-based navigation.

Findings

Real-time tracking of position, velocity, and pose on smartphones.

Effective bias correction improves inertial navigation accuracy.

Successful indoor dead-reckoning with consumer devices.

Abstract

Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in real-time and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.