DOI: 10.1109/iccas.2015.7364909
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Muhammad Ilyas, Seung-Ho Baeg, Sangdeok Park

Abstract: Position and attitude estimation of helmet-mounted imaging devices e.g. camerallidar is difficult in unstructured indoor environment due to lack of conventional localization systems, e.g. RF, Ultrasonic, UWB and Wi-Fi signals, usually available in modern office-like buildings. In this work, we use single MEMS IMU fitted on foot, which when combined with zero-velocity updates (ZUPT) in Extended Kalman filter estimation framework at every foot step, provides very accurate position estimates, regardless of the u…

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