面向城市测绘与目标跟踪研究的四旋翼无人机设计与飞行演示
Design and Flight Demonstration of a Quadrotor for Urban Mapping and Target Tracking Research
AI总结：
本文设计并演示了一种配备五摄像头和机载Jetson Orin Nano的小型四旋翼无人机，实现GPS目标自主跟踪与DSO环境建图，并通过飞行测试验证了系统性能。
摘要：
本文描述了一种配备成像传感器的小型四旋翼无人机的硬件设计与飞行演示，用于城市测绘、危险规避和目标跟踪研究。该飞行器装备了五个摄像头，包括两对鱼眼立体摄像头（可实现近乎全向视野）和一个两轴云台摄像头。机载NVIDIA Jetson Orin Nano计算机运行Robot Operating System软件，用于数据采集。实现了一种自主跟踪行为，以协调四旋翼无人机和云台摄像头的运动，从而跟踪移动的GPS坐标。通过一次飞行测试演示了数据采集系统，该测试跟踪了一辆带有GPS标签的移动车辆穿过一系列道路和停车场。使用Direct Sparse Odometry (DSO)算法从采集的图像中重建了环境地图。此外，还通过声学噪声、通信范围、悬停时的电池电压和最大速度测试对四旋翼无人机的性能进行了表征。
英文摘要：
This paper describes the hardware design and flight demonstration of a small quadrotor with imaging sensors for urban mapping, hazard avoidance, and target tracking research. The vehicle is equipped with five cameras, including two pairs of fisheye stereo cameras that enable a nearly omnidirectional view and a two-axis gimbaled camera. An onboard NVIDIA Jetson Orin Nano computer running the Robot Operating System software is used for data collection. An autonomous tracking behavior was implemented to coordinate the motion of the quadrotor and gimbaled camera to track a moving GPS coordinate. The data collection system was demonstrated through a flight test that tracked a moving GPS-tagged vehicle through a series of roads and parking lots. A map of the environment was reconstructed from the collected images using the Direct Sparse Odometry (DSO) algorithm. The performance of the quadrotor was also characterized by acoustic noise, communication range, battery voltage in hover, and maximum speed tests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
