Research | VLC-centric Indoor Localization

Acknowledgement

This project is mainly supported by the NSFC 61403325, and RGC-GRF grants.

Background

Localization is essential for many robotic tasks like planning and navigation, as well as for a wide range of location-based services in daily life, such as augmented reality and pedestrian way-finding on mobile devices. We are interested in drift-free global solutions in GPS-denied indoor environments. Further, we are motivated to find a lightweight solution that is accurate, reliable and more easily affordable by resource-constrained platforms like service robots and mobile devices. In recent years, visible light communication (VLC) has become a very promising technology for pervasive indoor global positioning with the widespread LED luminaires deployed in buildings. We aim to develop low-cost global localization solutions, lightweight and accurate for inexpensive mobile platforms working in daily scenarios, by integrating robotic perception and VLC technologies.

Our Vision

We see VLC as an appropriate communication technology to augment indoor localization performance on resource-constrained platforms while keeping the associated cost to a minimum, considering the flourish of LED lights on the lighting market. Benefiting from the research experiences in robotics for years, we also exploit multi-sensor fusion and SLAM technologies in our research. Our vision is towards a cost-effective and light-weight indoor localization solution that can make every mobile agent location-aware in modern buildings, with guaranteed accuracy, latency, and scalability.

Introduction

We started our research on VLC-based localization in 2013. The early works studied data-driven localization methods using a prototyping VLC network comprised of modulated LED tubes with customized driver circuitry, as well as a cheap photodiode sensor as the VLC receiver. In particular, we proposed a CDMA-based asynchronous communication method with multiple access support for VLC-based localization. After that, we presented a data-driven localization approach using Gaussian processes. We also demonstrated its application to path planning and indoor navigation tasks.

For the ongoing work, we have iterated the hardware design of our LED prototypes, as well as the low-level physical layer and MAC layer design of the VLC protocol. We are now leveraging more sensing modalities aside from VLC, such as inertial measurements and opportunistic signals, such as WiFi, Bluetooth, and geomagnetic fields. By fusing these heterogeneous sensing data soundly, we expect to develop accurate, lightweight localization systems of better robustness and usability in real situations.

Tightly-coupled VLC-inertial Localization

• Qing Liang and Ming Liu, Technical Report: A Tightly Coupled VLC-Inertial Localization System by EKF, TechReport pdf bibtex video

• Qing Liang, Jiahui Lin and Ming Liu, Towards Robust Visible Light Positioning Under LED Shortage by Visual-inertial Fusion, 2nd Runner-up, Best Paper Award, International Conference on Indoor Positioning and Indoor Navigation (IPIN 2019), Sep. 30- Oct. 03, 2019, Pisa, Italy pdf bibtex

Smartphone Localization using Opportunistic Signals

• Qing Liang and Ming Liu, An Automatic Site Survey Approach for Indoor Localization using a Smartphone, IEEE Transactions on Automation Science and Engineering (TASE), available online June 14, 2019   link pdf bibtex

• Qing Liang, Lujia Wang, Youfu Li, Ming Liu, Indoor Mapping and Localization for Pedestrians using Opportunistic Sensing with Smartphones, Finalist of ABB Best Student Paper Award IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)   link pdf

Data-driven Localization and Path Planning by VLC

• Qing Liang, Lujia Wang, Youfu Li, Ming Liu, Plugo: a Scalable Visible Light Communication System towards Low-cost Indoor Localization, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)     link pdf

• Kejie Qiu, Fangyi Zhang, Ming Liu, Let The Light Guide us: VLC-based localization, IEEE Robotics and Automation Magazine (RAM), Vol 23, Issue 4, pp 174-183, Dec 2016,   link

• Kejie Qiu, Fangyi Zhang, Ming Liu, Visible Light Communication-based Indoor Localization using Gaussian Process, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015,   link

• Fangyi Zhang, Kejie Qiu, Ming Liu, Asynchronous Blind Signal Decomposition Using Tiny-Length Code for Visible Light Communication-Based Indoor Localization, IEEE International Conference on Robotics and Automation (ICRA), 2015, Link, pdf bibtex

• Kejie Qiu, Fangyi Zhang, Ming Liu, Visible Light Communication-based Indoor Localization and Metric-free Path Planning, IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, 2015

• Ming Liu, Kejie Qiu, Fangyi Zhang, Visible Light Communication-aided Real-time Indoor Localization, Invited Paper for the 2015 International Conference on Real-time Computing and Robotics (RCAR 2015), June 23-26, 2015, Changsha, China

• Ming Liu, Kejie Qiu, Shaohua Li, Fengyu Che, Liang Wu, C. Patrick Yue, Towards Indoor Localization using Visible Light Communication for Consumer Electronic Devices, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014, pdf bibtex

Dataset

This dataset is taken in an environment with Visible Light Communication (VLC) light beacons, for the purpose of low-cost localization using VLC.

There are 70+ separate rosbags in the zip. The total length is over one hour.

Reference:

• Ming Liu, Kejie Qiu, Shaohua Li, Fengyu Che, Liang Wu, C. Patrick Yue, Towards Indoor Localization using Visible Light Communication for Consumer Electronic Devices, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2014, pdf bibtex

• Fangyi Zhang, Kejie Qiu, Ming Liu, Asynchronous Blind Signal Decomposition Using Tiny-Length Code for Visible Light Communication-Based Indoor Localization, IEEE International Conference on Robotics and Automation (ICRA), 2015, Link, pdf bibtex

• Kejie Qiu, Fangyi Zhang, Ming Liu, Visible Light Communication-based Indoor Localization and Metric-free Path Planning, IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden, 2015