  The advances in compact and agile micro aerial vehicles (MAVs) have shown
great potential in replacing human for labor-intensive or dangerous indoor
investigation, such as warehouse management and fire rescue. However, the
design of a state estimation system that enables autonomous flight in such dim
or smoky environments presents a conundrum: conventional GPS or computer vision
based solutions only work in outdoors or well-lighted texture-rich
environments. This paper takes the first step to overcome this hurdle by
proposing Marvel, a lightweight RF backscatter-based state estimation system
for MAVs in indoors. Marvel is nonintrusive to commercial MAVs by attaching
backscatter tags to their landing gears without internal hardware
modifications, and works in a plug-and-play fashion that does not require any
infrastructure deployment, pre-trained signatures, or even without knowing the
controller's location. The enabling techniques are a new backscatter-based pose
sensing module and a novel backscatter-inertial super-accuracy state estimation
algorithm. We demonstrate our design by programming a commercial-off-the-shelf
MAV to autonomously fly in different trajectories. The results show that Marvel
supports navigation within a range of $50$ m or through three concrete walls,
with an accuracy of $34$ cm for localization and $4.99^\circ$ for orientation
estimation, outperforming commercial GPS-based approaches in outdoors.
