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

• URL: http://arxiv.org/abs/2408.04797v1
• Date: Fri, 9 Aug 2024 00:30:22 GMT
• Title: AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems
• Authors: Sudeep Pasricha,
• Abstract summary: This article provides an overview of the challenges facing state-of-the-art indoor navigation solutions. It then describes how AI algorithms deployed on mobile embedded systems can overcome these challenges.
• Score: 3.943289808718775
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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