Pedestrian Gait Information Aided Visual Inertial SLAM for Indoor Positioning Using Handheld Smartphones

Simultaneous localization and mapping (SLAM) is currently a widely used technology for indoor positioning. Many studies have focused on the smartphone-based localization and navigation for its portability and feature-rich sensors. But due to the low-quality sensors and complex environments, current SLAM-based positioning technology using smartphones still poses great challenges, such as inherent cumulative global drift and potential divergence facing texture-less indoor regions. For the regular gait characteristics of pedestrians when naturally walking, the gait motion model can certainly provide effective observation of the pedestrian motion state. We propose a visual-inertial odometry (VIO) assisted by pedestrian gait information for smartphone-based indoor positioning. This work mainly builds two additional state constraints, pedestrian velocity, and step displacement, obtained by the pedestrian dead reckoning (PDR) algorithm for the visual-inertial tracking system. For each step, the corresponding residual term of step length and velocity constraints is constructed and added to the cost function for nonlinear sliding-window optimization. Furthermore, the step displacement is applied again in the four-degree-of-freedom (4-DOF) graph-based optimization to refine the trajectory. VIO system also assists the PDR algorithm in mode switching, to improve the accuracy of gait information by applying the adaptive step length formula. Field experiments were conducted, and the results indicate that with the aiding of the pedestrian gait information, the accuracy and robustness of the visual-inertial pedestrian tracking system using smartphones have been significantly improved. Compared with the state-of-the-art algorithm monocular visual-inertial navigation system (VINS-MONO), our method improves the accuracy by 54.6% on average in our field tests in challenging environments.