试验台
计算机科学
软件部署
比例(比率)
计算机视觉
工作区
惯性测量装置
人工智能
因子图
同时定位和映射
图形
机器人
电信
解码方法
理论计算机科学
移动机器人
地理
操作系统
地图学
计算机网络
作者
Liang Qing,Yuxiang Sun,Chengju Liu,Ming Liu,Lujia Wang
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:71: 1-12
被引量:7
标识
DOI:10.1109/tim.2021.3123293
摘要
Indoor localization of high accuracy has been widely interested. Among competitive solutions, visible light positioning (VLP) is promising due to its ability to deliver high-accuracy 3-D position and orientation with low-cost sensors by sharing the LED lighting infrastructure widespread in buildings. Most VLP systems require a prior LED location map for which manual surveys are costly in practical deployment at scale. In this article, to address this difficulty, we propose a novel system for efficient and accurate offline mapping of LEDs for VLP. With input from visual–inertial sensors and existing or surveyed priors, it builds the map by posing a full simultaneous localization and mapping (SLAM) problem within a factor graph formulation. Compared to manual surveys, it greatly saves human labor and time while yielding an accurate and workspace-aligned LED map. With real-world experiments in a room-scale testbed and a $15\times $ larger lab office, we extensively evaluate the LED mapping system to verify its efficacy and performance gains.
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