  This paper presents an indoor pose estimation system for micro aerial
vehicles (MAVs) with a single WiFi access point. Conventional approaches based
on computer vision are limited by illumination conditions and environmental
texture. Our system is free of visual limitations and instantly deployable,
working upon existing WiFi infrastructure without any deployment cost. Our
system consists of two coupled modules. First, we propose an angle-of-arrival
(AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA
for positioning. Second, we formulate a WiFi-inertial sensor fusion model that
fuses the AoA and the odometry measured by inertial sensors to optimize MAV
poses. Considering the practicality of MAVs, our system is designed to be
real-time and initialization-free for the need of agile flight in unknown
environments. The indoor experiments show that our system achieves the accuracy
of pose estimation with the position error of $61.7$ cm and the attitude error
of $0.92^\circ$.
