Low cost vision-aided IMU for pedestrian navigation

Abstract

Low cost MEMS sensors typically result in large position errors after very short periods of time unless they are frequently corrected by measurements from other systems. One form of measurements comes from the computer vision community where successive frames from a camera approximately looking at the ground can be used to compute the translation between frames. These measurements can be used to control the drift of an Inertial Measurement Unit (IMU) when measurements from other systems such as GPS are not available. This configuration of sensors is preferable since they are already available on some smartphones. This paper demonstrates that computer vision measurements can significantly reduce the drift of IMU-only positioning with a view for pedestrian navigation indoors. Issues such as computational requirements and operation in low light areas are also discussed.