A PDRVIO Loosely coupled Indoor Positioning System via Robust Particle Filter.

In recent years, the Visual Inertial Odometry (VIO) technology has attracted attention as a support technology that improves medical experience and management efficiency. However, the performance of most VIO systems will drop drastically when the light intensity changes significantly or there are few texture features from the images. This paper designs a visual-inertial fusion-based navigation Indoor Positioning system to deal with the challenging scenario. It loosely coupled an inertial sensor-based pedestrian dead reckoning (PDR) model with the VIO model via a robust particle filter. The state estimation of the particle filter is based on the PDR model. The VIO model is used for the measurements of the particle filter. It compensates the gross errors of the VIO with a visual error propagation model which is established according to the posterior observation residuals of visual feature points. It is verified through experiments that the PDR/VIO fusion indoor positioning system based on the robust particle filter implemented in this paper has improved positioning accuracy and strong ability to deal with complex scenes.