LAUSR

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.