WiSion: Bolstering MAV 3D Indoor State Estimation by Embracing Multipath of WiFi

Abstract

Recent years have witnessed a remarkable growth of micro aerial vehicle (MAV) technologies, which are desirable for many applications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , warehouse inventory management and home entertainment. These indoor needs significantly improve work efficiency while posing fundamental challenges in the design of MAV state estimation. The state includes a vehicle's position, orientation, and velocity, which are fundamental to guide the motor control and adjust that vehicle's actions in autonomous flight. Existing vision-based solutions only work in well-lit texture-rich environments, while laser-based solutions are limited to MAVs' payload and budget. This paper presents WiSion, a robust and low-cost state estimator that leverages ubiquitous WiFi to estimate six-degree-of-freedom states for MAVs. Our observation is that the multipath of WiFi conceals a wealth of information about a vehicle's state, which helps combat the temporal drift of inertial sensors for smooth state estimation. We realize WiSion by an absolute-relative WiFi sensing module and a WiFi-inertial state estimation module. It works without knowing access points' (APs') positions. We implement the prototype with off-the-shelf products and conduct experiments in indoor venues. Our results show that WiSion achieves the position error of 35.25 cm and the attitude error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2.6^\circ$</tex-math></inline-formula> with a maximum linear velocity of 1.74 m/s. Moreover, WiSion can recover APs' positions and is robust to indoor hindrances such as obstacles and multipath.