GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.

中文翻译：

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使用简单的配备摄像头的无人机在城市环境中进行自动地理定位：一种快速制图的替代方法？

在周围物体的几何形状和形态与GNSS信号相互作用的区域中，GNSS的定位精度可能会降低。具体而言，由于信号干扰或干扰，城市环境对精确的GNSS定位提出了挑战。此外，非GNSS勘测方法（包括全站仪和激光扫描仪）涉及到现场的耗时实践和昂贵的设备。本研究提出将无人机用于自动快速制图，该解决方案通过使用同步定位和制图（SLAM）在本地坐标系中获取地面上特征点的位置信息，从而解决了无人机自身的定位问题）和视觉算法。配备有摄像头和至少一个已知点的普通无人机，足以生成场景的局部图并估计预定义地面点的相对坐标以及其他任意点云。由此产生的点云很容易测量，用于从感兴趣区域提取和解释几何信息。在执行UAV相机测量的点几何的线和面对齐的两个新颖的优化程序下，一组实验确定了视觉点在距原点15 m的距离内的定位精度达到了50 cm以下。因此，具有光学传感器和视觉标记的简单无人机被证明在未知环境中进行快速制图和点定位非常有前途且具有成本效益。