In this paper, we propose LUVI, Lightweight UWB-VIO relative positioning method for indoor localization. Recent designs of handheld and embedded devices feature various technologies which have the means to enhance localization performance in indoor environments. These include visual odometry based on cameras and augmented reality, and communication hardware such as UWB. Integration of such technologies to exploit their advantages allows us to compensate for each other's errors in measurement. This improves the overall function of future services, such as visual representation of sensing information from sensors in areas that are not physically visible. However, existing work cannot fully exploit these technologies to high extent, often inducing high errors or wasted resources. LUVI is a novel localization method which estimates the location of a target object using relative coordinates of estimator devices without the aid of definitive coordinates. LUVI focuses on utilization of lightweight management of virtual anchors for localization, with functions that reduce the computing and communication complexity while maintaining the accuracy and improving energy efficiency of the localization. Our work has been fully implemented and tested in several indoor environments, showing robustness to NLOS while significantly reducing computational complexity, and up to 30% lower average error.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT) (NRF2020R1A2C1102284).Young-Bae Ko received his Ph.D. degree in computer science from Texas A\&M University, College Station, TX, USA in August 2000. He then joined IBM Thomas J. Watson Research Center, Yorktown Heights, NY, as a Research Staff Member. Dr. Ko is currently a full professor with the Department of Software and Computer Engineering, Ajou University, Korea, leading the Intelligence of Connected \& Convergence Systems (iCONS) laboratory funded by the Brain Korea 21 (BK21) National Project. The main areas of his research interest are the convergence of XR and localization, AI-empowered wireless communications(6 G), and Multi-UAV networks.
