Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 201-205, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1/201/2014/
doi:10.5194/isprsarchives-XL-1-201-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
07 Nov 2014
Point Cloud Generation from sUAS-Mounted iPhone Imagery: Performance Analysis
A. D. Ladai and J. Miller Towill, Inc., 2300 Clayton Road, Suite 1200, Concord, CA 94520-2176, USA
Keywords: small UAS, iPhone, point cloud generation, ortho photo, quality control Abstract. The rapidly growing use of sUAS technology and fast sensor developments continuously inspire mapping professionals to experiment with low-cost airborne systems. Smartphones has all the sensors used in modern airborne surveying systems, including GPS, IMU, camera, etc. Of course, the performance level of the sensors differs by orders, yet it is intriguing to assess the potential of using inexpensive sensors installed on sUAS systems for topographic applications. This paper focuses on the quality analysis of point clouds generated based on overlapping images acquired by an iPhone 5s mounted on a sUAS platform. To support the investigation, test data was acquired over an area with complex topography and varying vegetation. In addition, extensive ground control, including GCPs and transects were collected with GSP and traditional geodetic surveying methods. The statistical and visual analysis is based on a comparison of the UAS data and reference dataset. The results with the evaluation provide a realistic measure of data acquisition system performance. The paper also gives a recommendation for data processing workflow to achieve the best quality of the final products: the digital terrain model and orthophoto mosaic.

After a successful data collection the main question is always the reliability and the accuracy of the georeferenced data.

Conference paper (PDF, 775 KB)


Citation: Ladai, A. D. and Miller, J.: Point Cloud Generation from sUAS-Mounted iPhone Imagery: Performance Analysis, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1, 201-205, doi:10.5194/isprsarchives-XL-1-201-2014, 2014.

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