AI-Driven Indoor Navigation with Mobile Embedded Systems

被引：0

作者：

Pasricha, Sudeep ^{[1
]}

机构：

[1] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80523 USA

来源：

2024 INTERNATIONAL CONFERENCE ON HARDWARE/SOFTWARE CODESIGN AND SYSTEM SYNTHESIS, CODES+ISSS 2024 | 2024年

关键词：

indoor navigation; artificial intelligence; machine learning; embedded systems; energy efficiency; edge computing; IoT computing; reliability; security; uncertainty resilience; LOCALIZATION;

D O I：

10.1109/CODES-ISSS60120.2024.00016

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：47 / 48

页数：2

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