Novel velocity model to improve indoor localization using inertial navigation with sensors on a smartphone

TL;DR

This paper introduces a generalized velocity model for smartphone-based inertial navigation systems to enhance indoor localization accuracy by reducing sensor drift and applying periodic corrections.

Contribution

It proposes a Gaussian velocity model that improves localization accuracy and robustness, outperforming traditional methods with fewer correction steps.

Findings

Gaussian velocity model achieves 50% less variance than Step and Heading approach

Minimum 5% correction frequency per step improves accuracy

Method effectively reduces sensor drift in indoor localization

Abstract

We present a generalized velocity model to improve localization when using an Inertial Navigation System (INS). This algorithm was applied to correct the velocity of a smart phone based indoor INS system to increase the accuracy by counteracting the accumulation of large drift caused by sensor reading errors. We investigated the accuracy of the algorithm with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Our results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 50\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a minimum of 5\% corrections per step is sufficient for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues.