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LedMapper: Toward Efficient and Accurate LED Mapping for Visible Light Positioning at Scale

  • Qing Liang
  • , Yuxiang Sun
  • , Chengju Liu
  • , Ming Liu*
  • , Lujia Wang
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

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 15x larger lab office, we extensively evaluate the LED mapping system to verify its efficacy and performance gains. © 2021 IEEE.
Original languageEnglish
Article number8500612
JournalIEEE Transactions on Instrumentation and Measurement
Volume71
Online published30 Nov 2021
DOIs
Publication statusPublished - 2022
Externally publishedYes

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant U1713211, in part by the Zhongshan Municipal Science and Technology Bureau Fund under Project ZSST21EG06, in part by the Collaborative Research Fund by Research Grants Council Hong Kong under Project C4063-18G, in part by the Department of Science and Technology of Guangdong Province Fund under Project GDST20EG54, and in part by the Foshan-HKUST Projects University Enterprise Collaborative Research and Development Program under Project FSUST20-SHCIRI06C, awarded to Prof. M. Liu.

Research Keywords

  • Factor graph optimization
  • indoor localization
  • LED mapping
  • visible light communication (VLC)
  • visible light positioning (VLP)
  • visual-inertial odometry (VIO)

RGC Funding Information

  • RGC-funded

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