  Autonomous Micro Aerial Vehicles are deployed for a variety tasks including
surveillance and monitoring. Perching and staring allow the vehicle to monitor
targets without flying, saving battery power and increasing the overall mission
time without the need to frequently replace batteries. This paper addresses the
Active Visual Perching (AVP) control problem to autonomously perch on inclined
surfaces up to $90^\circ$. Our approach generates dynamically feasible
trajectories to navigate and perch on a desired target location, while taking
into account actuator and Field of View (FoV) constraints. By replanning in
mid-flight, we take advantage of more accurate target localization increasing
the perching maneuver's robustness to target localization or control errors. We
leverage the Karush-Kuhn-Tucker (KKT) conditions to identify the compatibility
between planning objectives and the visual sensing constraint during the
planned maneuver. Furthermore, we experimentally identify the corresponding
boundary conditions that maximizes the spatio-temporal target visibility during
the perching maneuver. The proposed approach works on-board in real-time with
significant computational constraints relying exclusively on cameras and an
Inertial Measurement Unit (IMU). Experimental results validate the proposed
approach and shows the higher success rate as well as increased target
interception precision and accuracy with respect to a one-shot planning
approach, while still retaining aggressive capabilities with flight envelopes
that include large excursions from the hover position on inclined surfaces up
to 90$^\circ$, angular speeds up to 750~deg/s, and accelerations up to
10~m/s$^2$.
