Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

TL;DR

This paper develops a real-time visual place recognition system for indoor personal localization on smartphones, combining and improving existing algorithms to achieve fast, scalable, and accurate localization using limited mobile device resources.

Contribution

It introduces FastABLE, a modified ABLE-M algorithm optimized for mobile devices, enabling real-time, scalable indoor localization with significant processing improvements.

Findings

FastABLE outperforms original ABLE-M in speed and scalability.

The system achieves real-time localization on smartphones.

It effectively handles long image sequences with limited computing power.

Abstract

The paper presents an approach to indoor personal localization on a mobile device based on visual place recognition. We implemented on a smartphone two state-of-the-art algorithms that are representative to two different approaches to visual place recognition: FAB-MAP that recognizes places using individual images, and ABLE-M that utilizes sequences of images. These algorithms are evaluated in environments of different structure, focusing on problems commonly encountered when a mobile device camera is used. The conclusions drawn from this evaluation are guidelines to design the FastABLE system, which is based on the ABLE-M algorithm, but introduces major modifications to the concept of image matching. The improvements radically cut down the processing time and improve scalability, making it possible to localize the user in long image sequences with the limited computing power of a mobile device. The resulting place recognition system compares favorably to both the ABLE-M and the FAB-MAP solutions in the context of real-time personal localization.