Recent years have witnessed a remarkable growth of micro aerial vehicle (MAV) technologies, which are desirable for many applications, e.g., 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 $2.6^\circ$ 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.