Over the past decades, quadcopters have been investigated, due to their mobility and flexibility to operate in a wide range of environments. They have been used in various areas, including surveillance and monitoring. During a mission, drones do not have to remain active once they have reached a target location. To conserve energy and maintain a static position, it is possible to perch and stop the motors in such situations. The problem of achieving a reliable and highly accurate perching method remains a challenge and promising. In this paper, a vision-based autonomous perching approach for nano quadcopters onto a predefined perching target on horizontal surfaces is proposed. First, a perching target with a small marker inside a larger one is designed to improve detection capability at a variety of ranges. Second, a monocular camera is used to calculate the relative poses of the flying vehicle from the markers detected. Then, a Kalman filter is applied to determine the pose more reliably, especially when measurement data is missing. Next, we introduce an algorithm for merging the pose data from multiple markers. Finally, the poses are sent to the perching planner to conduct the real flight test to align the drone with the target's center and steer it there. Based on the experimental results, the approach proved to be effective and feasible. The drone can successfully perch on the center of markers within two centimeters of precision.

翻译：过去数十年来，四旋翼飞行器因其在多种环境中的机动性和灵活性而受到广泛研究，并被应用于监视与监测等多个领域。在执行任务时，无人机到达目标位置后无需始终保持飞行状态。为节省能量并维持静态位置，此类情况下可通过栖息并关闭电机实现。然而，实现可靠且高精度的栖息方法仍具挑战性且具有重要研究价值。本文提出一种基于视觉的纳米四旋翼飞行器自主栖息方法，使其能够栖息于水平表面上的预设栖息目标。首先，设计了一种内含小型标记的大型嵌套式栖息目标，以提升不同距离下的检测能力。其次，利用单目相机从检测到的标记中计算飞行器的相对位姿。随后，采用卡尔曼滤波器更可靠地确定位姿，尤其在测量数据缺失时。接着，引入一种融合多个标记位姿数据的算法。最后，将位姿信息发送至栖息规划器，执行实际飞行测试，使无人机对准目标中心并引导其完成栖息。实验结果表明，该方法有效且可行，无人机可成功栖息于标记中心，定位精度在2厘米以内。