  Autonomous Nano Aerial Vehicles have been increasingly popular in
surveillance and monitoring operations due to their efficiency and
maneuverability. Once a target location has been reached, drones do not have to
remain active during the mission. It is possible for the vehicle to perch and
stop its motors in such situations to conserve energy, as well as maintain a
static position in unfavorable flying conditions. In the perching target
estimation phase, the steady and accuracy of a visual camera with markers is a
significant challenge. It is rapidly detectable from afar when using a large
marker, but when the drone approaches, it quickly disappears as out of camera
view. In this paper, a vision-based target poses estimation method using
multiple markers is proposed to deal with the above-mentioned problems. First,
a perching target with a small marker inside a larger one is designed to
improve detection capability at wide and close ranges. Second, the relative
poses of the flying vehicle are calculated from detected markers using a
monocular camera. Next, a Kalman filter is applied to provide a more stable and
reliable pose estimation, especially when the measurement data is missing due
to unexpected reasons. Finally, we introduced an algorithm for merging the
poses data from multi markers. The poses are then sent to the position
controller to align the drone and the marker's center and steer it to perch on
the target. The experimental results demonstrated the effectiveness and
feasibility of the adopted approach. The drone can perch successfully onto the
center of the markers with the attached 25mm-diameter rounded magnet.
