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A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements

机译:低成本多传感器移动制图系统及其在树测量中的可行性

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This paper presents a novel low-cost mini-UAV-based laser scanning system, which is also capable of performing car-based mobile mapping. The quality of the system and its feasibility for tree measurements was tested using the system's laser scanner. The system was constructed as a modular measurement system consisting of a number of measurement instruments: a GPS/IMU positioning system, two laser scanners, a CCD camera, a spectrometer and a thermal camera. An Ibeo Lux and a Sick LMS151 profile laser were integrated into the system to provide dense point clouds; intensities of the reflected echoes can also be obtained with the Sick LMS. In our tests, when using a car as a platform, the pole-type object extraction algorithm which was developed resulted in 90% completeness and 86% correctness. The heights of pole-type objects were obtained with a bias of-1.6 cm and standard deviation of 5.4 cm. Using a mini-UAV as the platform, the standard deviation of individual tree heights was about 30 cm. Also, a digital elevation model extraction was tested with the UAV data, resulting in a height offset of about 3.1 cm and a standard deviation of 9.2 cm. With a multitemporal point cloud, we demonstrated a method to derive the biomass change of a coniferous tree with an R~2 value of 0.92. The proposed system is capable of not only recording point cloud data giving the geometry of the objects, but also simultaneously collecting image data, including overlapping images and the intensity of laser backscatter, as well as hyperspectral and thermal data. Therefore we believe that the system is feasible for new algorithm and concept development and for basic research, especially when data is recorded multitemporally.
机译:本文提出了一种新颖的低成本基于微型UAV的激光扫描系统,该系统还能够执行基于汽车的移动制图。使用系统的激光扫描仪测试了系统的质量及其在树木测量中的可行性。该系统被构造为一个模块化的测量系统,包括许多测量仪器:GPS / IMU定位系统,两个激光扫描仪,CCD照相机,光谱仪和热像仪。系统中集成了Ibeo Lux和Sick LMS151轮廓激光器,以提供密集的点云。反射回波的强度也可以通过Sick LMS获得。在我们的测试中,当以汽车为平台时,开发的极点型对象提取算法可实现90%的完整性和86%的正确性。杆状物体的高度的偏差为-1.6 cm,标准偏差为5.4 cm。使用微型无人机作为平台,单个树高的标准偏差约为30厘米。此外,使用UAV数据测试了数字高程模型提取,结果得到了约3.1 cm的高度偏移和9.2 cm的标准偏差。利用多时相点云,我们证明了一种得出针叶树生物量变化的方法,R〜2值为0.92。所提出的系统不仅能够记录给出物体几何形状的点云数据,而且能够同时收集图像数据,包括重叠图像和激光反向散射的强度,以及高光谱和热数据。因此,我们认为该系统对于新算法和概念开发以及基础研究是可行的,尤其是在多时间记录数据时。

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