Pose estimation of head-mounted imaging device by transfer alignment technique in unstructured indoor environment

Abstract

Position and attitude estimation of helmet-mounted imaging devices e.g. camera/lidar 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 user and environment. We also present a novel method to reduce the error drift in ZUPT-only position estimates by employing the pitching motion of the foot during the swing phase. Another small IMU is attached on helmet, which can provide attitude information of imaging device at most at its own. However, for proper mapping applications, both position and attitude information of the imaging device is required at high rate, which is difficult to obtain from helmet IMU alone. To get complete pose information of the imaging device, we make use of the so called, ‘Transfer Alignment’ techniques, borrowed from avionics community. Experimental results show that poses of the imaging device is obtained with sufficient accuracy for mapping application without any extra sensors network aiding.