PALMS: Plane-based Accessible Indoor Localization Using Mobile Smartphones

TL;DR

PALMS is a novel indoor localization system for smartphones that uses floor plans and a particle filter with CES constraints, achieving improved accuracy and efficiency without prior environmental fingerprinting.

Contribution

Introduces a particle filter initialization method using CES constraints and orientation matching, enhancing indoor localization accuracy and reducing convergence time.

Findings

Outperforms traditional uniform particle filter methods.

Eliminates the need for environmental fingerprinting.

Provides scalable and practical indoor navigation.

Abstract

In this paper, we present PALMS, an innovative indoor global localization and relocalization system for mobile smartphones that utilizes publicly available floor plans. Unlike most vision-based methods that require constant visual input, our system adopts a dynamic form of localization that considers a single instantaneous observation and odometry data. The core contribution of this work is the introduction of a particle filter initialization method that leverages the Certainly Empty Space (CES) constraint along with principal orientation matching. This approach creates a spatial probability distribution of the device's location, significantly improving localization accuracy and reducing particle filter convergence time. Our experimental evaluations demonstrate that PALMS outperforms traditional methods with uniformly initialized particle filters, providing a more efficient and accessible approach to indoor wayfinding. By eliminating the need for prior environmental fingerprinting, PALMS provides a scalable and practical approach to indoor navigation.