Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

Abstract

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft®’s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation.

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

“…This means that thinner branches will likely remain undetected than the up to 50 times wider tree trunks near to the ground. As discussed in [Rosnell and Honkavaara, 2012], we can confirm that the strong threedimensionality of forests poses challenges for SFM processing and data acquisition. After testing various flight patterns we can conclude that generally reconstruction becomes more challenging at lower altitudes, while at higher altitudes accuracy decreases due reduced ground sampling distance.…”

“…By now fewer attempts have been conducted to extract vegetation structure or specifically information on forest stands. Previous studies indicated difficulties and limitations of this technique Lucieer, 2012, Rosnell andHonkavaara, 2012] in complex and heterogeneous spatial structures such as vegetation. Thus, 3D-reconstruction of vegetation requires an approach which differs from the above mentioned objects in terms of the data acquisition setup and data processing and its parameters.…”

“…UAVs of different size and technology have been used in applications such as precision agriculture [Valente et al, 2013, Zhang andKovacs, 2012], fire monitoring [Merino et al, 2012] and forest characterization [Tao et al, 2011, Wallace et al, 2012. Payload used range from off-the-shelf digital cameras [Rosnell and Honkavaara, 2012] to FLIR systems [Turner et al, 2011] and laser scanners [Wallace et al, 2012]. In addition more and more efficient algorithms for transforming the enormous amount of collected images into 3D information become available for public through web services or open source tools.…”

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

“…Personal remote sensing systems have enabled accurate mapping of canopy height and biomass density as well as the discrimination of individual tree structural, spectral, and phenological traits [14,[21][22][23]. Similar systems have also been used for mapping stream channel geomorphology [24,25], vineyard and orchard plant structure [26][27][28], the topography of bare substrates [29][30][31][32][33][34], and lichen and moss extent and coverage [16].…”

Modelling Steep Surfaces by Various Configurations of Nadir and Oblique Photogrammetry

“…Recent literature shows how UASs are becoming the most useful solution for pit monitoring. The topics covered are: camera and system calibration (Shahbazi, 2015), imagery bundle adjustment and orientation quality assessment (Rosnell, 2012;Greiwe, 2013Tong, 2015, point cloud generation (Sauerbier, 2011;Rosnell, 2012;Shahbazi, 2015), the attainable products (digital models and orthophotos), data validation (Niethammer, 2012;Bemis, 2014;. The use of oblique imagery is only initial (Greiwe et al 2013).…”