AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems

TL;DR

This paper reviews how AI algorithms integrated into mobile embedded systems can address the challenges of indoor localization and navigation without GPS, using wireless signals and sensors.

Contribution

It provides an overview of challenges and discusses AI-based solutions for indoor navigation using mobile embedded systems.

Findings

AI algorithms improve indoor localization accuracy

Mobile embedded systems enable real-time navigation

Wireless signals and sensors are effectively utilized

Abstract

Indoor navigation is a foundational technology to assist the tracking and localization of humans, autonomous vehicles, drones, and robots in indoor spaces. Due to the lack of penetration of GPS signals in buildings, subterranean locales, and dense urban environments, indoor navigation solutions typically make use of ubiquitous wireless signals (e.g., WiFi) and sensors in mobile embedded systems to perform tracking and localization. This article provides an overview of the many challenges facing state-of-the-art indoor navigation solutions, and then describes how AI algorithms deployed on mobile embedded systems can overcome these challenges.