Enhancing Indoor Inertial Odometry with WiFi

Abstract

Accurately measuring the distance traversed by a subject, commonly referred to as odometry, in indoor environments is of fundamental importance in many applications such as augmented and virtual reality tracking, indoor navigation, and robot route guidance. While theoretically, odometry can be performed using a simple accelerometer, practically, it is well-known that the distances measured using accelerometers suffer from large drift errors. In this paper, we propose WIO, a WiFi-assisted Inertial Odometry technique that uses WiFi signals as an auxiliary source of information to correct these drift errors. The key intuition behind WIO is that among multiple reflections of a transmitted WiFi signal arriving at the WiFi receiver, WIO first isolates one reflection and then measures the change in the length of the path of that reflection as the subject moves. By identifying the extent through which the length of the path of that reflection changes, along with the direction of motion of the subject relative to that path, WIO can estimate the distance traversed by the subject using WiFi signals. WIO then uses this distance estimate to correct the drift errors. While researchers have previously proposed to use WiFi signals to correct drift errors, prior schemes suffer from one or more of the following six limitations: they 1) do not work indoors, 2) require manual exhaustive fingerprinting, 3) are not resilient against changes in environment including human movements, 4) do not work on commodity WiFi devices, 5) require multiple access points, and/or 6) can measure distance traversed by humans but not by non-human subjects. WIO addresses all of these limitations. We implemented WIO using commodity devices, and extensively evaluated it in a wide variety of complex indoor scenarios on both human and robotic subjects. Our results demonstrate that WIO achieved an average error of just 6.28% in estimating the distances traversed by the subjects.