The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B1-2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-343-2020
https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-343-2020
06 Aug 2020
 | 06 Aug 2020

ON PRACTICAL ACCURACY ASPECTS OF UNMANNED AERIAL VEHICLES EQUIPPED WITH SURVEY GRADE LASER SCANNERS

K. Nakano, Y. Tanaka, H. Suzuki, K. Hayakawa, and M. Kurodai

Keywords: Unmanned aerial vehicle, Survey grade laser scanner, Accuracy evaluation, Theoretical value, Point cloud, Photogrammetry

Abstract. Unmanned aerial vehicles (UAVs) equipped with image sensors, which have been widely used in various fields such as construction, agriculture, and disaster management, can obtain images at the millimeter to decimeter scale. Useful tools that produce realistic surface models using 3D reconstruction software based on computer vision technologies are generally used to produce datasets from acquired images using UAVs. However, it is difficult to obtain the feature points from surfaces with limited texture, such as new asphalt or concrete, or detect the ground in areas such as forests, which are commonly concealed by vegetation. A promising method to address such issues is the use of UAV-equipped laser scanners. Recently, low and high performance products that use direct georeferencing devices integrated with laser scanners have been available. Moreover, there have been numerous reports regarding the various applications of UAVs equipped with laser scanners; however, these reports only discuss UAVs as measuring devices. Therefore, to understand the functioning of UAVs equipped with laser scanners, we investigated the theoretical accuracy of the survey grade laser scanner unit from the viewpoint of photogrammetry. We evaluated the performance of the VUX-1HA laser scanner equipped on a Skymatix X-LS1 UAV at a construction site. We presented the theoretical values obtained using the observation equations and results of the accuracy aspects of the acquired data in terms of height.