IEEE Robotics and Automation Letters · 2021 · 16 citations · 20 references
ComputingAutonomous SystemsRobotics EngineeringComputer VisionRobot VisionSensor FusionIndoor LocalizationPose EstimationRobot LocalizationSensor Fusion For LocalizationVisual LocalizationVisual SlamInertial Measurement Unit FusionKalman Filter FusionLocalization FusionLocalization RobustnessLong-term LocalizationMulti-sensor Fusion LocalizationSensor Fusion LocalizationVisual-inertial Localization
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.
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