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

Qing Liang, Yuxiang Sun, Chengju Liu, Ming Liu, Lujia Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


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 15times larger lab office, we extensively evaluate the LED mapping system to verify its efficacy and performance gains.

Original languageEnglish
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusPublished - Nov 2021


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

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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