AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems
This addresses indoor navigation for humans, autonomous vehicles, drones, and robots, but appears to be an incremental overview rather than a novel contribution.
The paper tackles the problem of indoor localization and navigation in GPS-denied environments by leveraging AI algorithms on mobile embedded systems, but does not provide specific results or numbers.
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.