# RINS-W: Robust Inertial Navigation System on Wheels

## TL;DR

This paper introduces RINS-W, a real-time inertial navigation system for wheeled robots that combines deep learning detectors with Kalman filtering to achieve accurate localization using only an IMU over long distances.

## Contribution

It presents the first integration of deep neural networks with advanced filtering for pure inertial navigation on wheeled vehicles.

## Key findings

- Achieves 20 m accuracy over 21 km trajectory.
- Demonstrates robustness with moderate-precision IMUs.
- First to combine deep learning with filtering for inertial navigation.

## Abstract

This paper proposes a real-time approach for long-term inertial navigation based only on an Inertial Measurement Unit (IMU) for self-localizing wheeled robots. The approach builds upon two components: 1) a robust detector that uses recurrent deep neural networks to dynamically detect a variety of situations of interest, such as zero velocity or no lateral slip; and 2) a state-of-the-art Kalman filter which incorporates this knowledge as pseudo-measurements for localization. Evaluations on a publicly available car dataset demonstrates that the proposed scheme may achieve a final precision of 20 m for a 21 km long trajectory of a vehicle driving for over an hour, equipped with an IMU of moderate precision (the gyro drift rate is 10 deg/h). To our knowledge, this is the first paper which combines sophisticated deep learning techniques with state-of-the-art filtering methods for pure inertial navigation on wheeled vehicles and as such opens up for novel data-driven inertial navigation techniques. Moreover, albeit taylored for IMU-only based localization, our method may be used as a component for self-localization of wheeled robots equipped with a more complete sensor suite.