Multi-sensor-based Target Pose Estimation for Autonomous Precision Perching of Nano Aerial Vehicles

Abstract

Nano Aerial Vehicles have become widely used for a variety of complex missions due to their mobility and the ability to access hard-to-reach areas. In most cases, these tasks require the vehicles to land on ground targets or perch on platforms mounted on diverse surfaces. Considering the surface the vehicle will reach, controlling perching is obviously a challenging task. Besides, the reliability of target position and direction estimation has a significant impact on perching performance. In this paper, a multi-sensor-based target pose estimation for autonomous precision perching of nano drones is proposed. First, the perching target, a cube cage containing a small marker inside a larger one, is designed to enhance pose estimation capability at a wide range of distances. Second, we constructed a nano drone with an upward monocular camera and a 5-direction multi-ranger deck. Next, the flying vehicle’s pose toward the perching target is calculated, followed by a Kalman filter for filtering and estimating the missing data. Finally, we introduced an algorithm to merge the pose data from multiple sensors when drones approach close to the target. Real measurements are conducted on the testbed. The experimental results demonstrated the utility and potential of the adopted approach with millimeter-level precision.