Tightly-Coupled Magneto-Visual-Inertial Fusion for Long Term Localization in Indoor Environment

作者：Jade Coulin, Richard Guillemard, Vincent Gay‐Bellile, Cyril Joly, Arnaud de La Fortelle · 发表于：IEEE Robotics and Automation Letters · 年份：2021 · DOI：10.1109/lra.2021.3136241 · 被引用次数：17 · 研究领域：Robotics and Sensor-Based Localization、Indoor and Outdoor Localization Technologies、Underwater Vehicles and Communication Systems

We propose in this letter a tightly-coupled fusion of visual, inertial and magnetic data for long-term localization in indoor environment. Unlike state-of-the-art Visual-Inertial SLAM (VISLAM) solutions that reuse visual map to prevent drift, we present in this letter an extension of the Multi-State Constraint Kalman Filter (MSCKF) that takes advantage of a magnetic map. It makes our solution more robust to variations of the environment appearance. The experimental results demonstrate that the localization accuracy of the proposed approach is almost the same over time periods longer than a year.