A Novel Inertial-Aided Visible Light Positioning System Using Modulated LEDs and Unmodulated Lights as Landmarks

Indoor localization with high accuracy and efficiency has attracted much attention. Due to visible light communication (VLC), the LED lights in buildings, once modulated, hold great potential to be ubiquitous indoor localization infrastructure. However, this entails retrofitting the lighting system and is hence costly in wide adoption. To alleviate this problem, we propose to exploit modulated LEDs and existing unmodulated lights as landmarks. On this basis, we present a novel inertial-aided visible light positioning (VLP) system for lightweight indoor localization on resource-constrained platforms, such as service robots and mobile devices. With blob detection, tracking, and VLC decoding on rolling-shutter camera images, a visual front end extracts two types of blob features, i.e., mapped landmarks (MLs) and opportunistic features (OFs). These are tightly fused with inertial measurements in a stochastic cloning sliding-window extended Kalman filter (EKF) for localization. We evaluate the system by extensive experiments. The results show that it can provide lightweight, accurate, and robust global pose estimates in real time. Compared with our previous ML-only inertial-aided VLP solution, the proposed system has superior performance in terms of positional accuracy and robustness under challenging light configurations, such as sparse ML/OF distribution. Note to Practitioners—This article is motivated by the problem that many existing visible light positioning (VLP) systems require high-cost environmental modifications, i.e., replacing a large portion of original lights with modulated LEDs as beacons. To reduce costs in wide adoption, we seek to use fewer modulated LEDs if possible. Accordingly, we present a novel inertial-aided VLP system that uses both modulated LEDs and unmodulated lights as landmarks. Like in other VLP systems, the successfully decoded LEDs provide absolute pose measurements for global localization. Unmodulated lights and the LEDs with decoding failures provide relative motion constraints, allowing the reduction of pose drift during the outage of modulated LEDs. Due to the tightly coupled sensor fusion by filtering, the system can provide efficient and accurate localization when modulated LEDs are sparse. The system is lightweight to run on resource-constrained platforms. For practical deployment of our system at scale, creating LED maps accurately and efficiently remains a problem. It is desired to develop automated LED mapping solutions in future work.