Unmanned micro air vehicles (MAVs) will play an important role in future reconnaissance and search and rescue applications.
In order to conduct persistent surveillance and to conserve energy, MAVs need the ability to land, and they need
the ability to enter (ingress) buildings and other structures to conduct reconnaissance. To be safe and practical under a
wide range of environmental conditions, landing and ingress maneuvers must be autonomous, using real-time, onboard
sensor feedback. To address these key behaviors, we present a novel method for vision-based autonomous MAV landing
and ingress using a single camera for two urban scenarios: landing on an elevated surface, representative of a rooftop,
and ingress through a rectangular opening, representative of a door or window. Real-world scenarios will not include special
navigation markers, so we rely on tracking arbitrary scene features; however, we do currently exploit planarity of the
scene. Our vision system uses a planar homography decomposition to detect navigation targets and to produce approach
waypoints as inputs to the vehicle control algorithm. Scene perception, planning, and control run onboard in real-time;
at present we obtain aircraft position knowledge from an external motion capture system, but we expect to replace this in
the near future with a fully self-contained, onboard, vision-aided state estimation algorithm. We demonstrate autonomous
vision-based landing and ingress target detection with two different quadrotor MAV platforms. To our knowledge, this is
the first demonstration of onboard, vision-based autonomous landing and ingress algorithms that do not use special purpose
scene markers to identify the destination.