AI与机器学习驱动的移动嵌入式系统室内定位与导航
AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems
AI总结：
该文综述室内导航因GPS失效而依赖WiFi与传感器的现状，分析前沿方案挑战，并说明移动嵌入式系统上的AI算法如何实现定位与导航。
摘要：
室内导航是一项基础技术，用于辅助在室内空间中对人员、自动驾驶车辆、无人机和机器人进行追踪与定位。由于GPS信号无法穿透建筑物、地下场所以及密集城市环境，室内导航解决方案通常利用移动嵌入式系统中无处不在的无线信号（例如WiFi）和传感器来执行追踪与定位。本文概述了最先进的室内导航解决方案所面临的诸多挑战，随后描述了部署在移动嵌入式系统上的AI算法如何克服这些挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
