  The advances in agile micro aerial vehicles (MAVs) have shown great potential
in replacing humans 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 poses fundamental challenges
in such dim or smoky environments. Current dominated computer-vision based
solutions only work in well-lighted texture-rich environments. This paper
addresses the challenge by proposing Marvel, an RF backscatter-based state
estimation system with online initialization and calibration. 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 with an automatic initialization module. Marvel is enabled by three new
designs, a backscatter-based pose sensing module, an online initialization and
calibration module, and a backscatter-inertial super-accuracy state estimation
algorithm. We demonstrate our design by programming a commercial 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 degrees for orientation
estimation. We further demonstrate our online initialization and calibration by
comparing to the perfect initial parameter measurements from burdensome manual
operations.
