Substantial progress has been made recently towards designing,
building and test-flying remotely piloted Micro Air Vehicles (MAVs)
and small UAVs. We seek to complement this progress in overcoming the
aerodynamic obstacles to flight at very small scales with a
vision-guided flight stability and autonomy system, based on a robust
horizon detection algorithm. In this paper, we first motivate the use
of computer vision for MAV autonomy, arguing that given current sensor
technology, vision may be the only practical approach to the
problem. We then describe our statistical vision-based horizon
detection algorithm, which has been demonstrated at 30Hz with over
99.9% correct horizon identification. Next, we develop robust schemes
for the detection of extreme MAV attitudes, where no horizon is
visible, and for the detection of horizon estimation errors, due to
external factors such as video transmission noise. Finally, we discuss
our feedback controller for self-stabilized flight, and report results
on vision-based autonomous flights of duration exceeding ten minutes.
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