GEOREFERENCING UAS DERIVATIVES THROUGH POINT CLOUD REGISTRATION WITH ARCHIVED LIDAR DATASETS

• Published in: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences remote sensing and spatial information sciences, 2016-10, Vol.IV-2/W1, p.195-199
• Main Authors: Magtalas, M. S. L. Y., Aves, J. C. L., Blanco, A. C.
• Format: Article
• Language: English
• Subjects: Datasets; Derivatives; Feature extraction; Global positioning systems; GPS; Ground based control; Iterative algorithms; Lidar; Mathematical models; Measuring instruments; Post-production processing; Quality; Satellite navigation systems; Source code; Spatial analysis; Spatial data; Three dimensional models; Unmanned aerial vehicles; Workflow
• ISSN: 2194-9050; 2194-9042; 2194-9050
• DOI: 10.5194/isprs-annals-IV-2-W1-195-2016

Georeferencing gathered images is a common step before performing spatial analysis and other processes on acquired datasets using unmanned aerial systems (UAS). Methods of applying spatial information to aerial images or their derivatives is through onboard GPS (Global Positioning Systems) geotagging, or through tying of models through GCPs (Ground Control Points) acquired in the field. Currently, UAS (Unmanned Aerial System) derivatives are limited to meter-levels of accuracy when their generation is unaided with points of known position on the ground. The use of ground control points established using survey-grade GPS or GNSS receivers can greatly reduce model errors to centimeter levels. However, this comes with additional costs not only with instrument acquisition and survey operations, but also in actual time spent in the field. This study uses a workflow for cloud-based post-processing of UAS data in combination with already existing LiDAR data. The georeferencing of the UAV point cloud is executed using the Iterative Closest Point algorithm (ICP). It is applied through the open-source CloudCompare software (Girardeau-Montaut, 2006) on a ‘skeleton point cloud’. This skeleton point cloud consists of manually extracted features consistent on both LiDAR and UAV data. For this cloud, roads and buildings with minimal deviations given their differing dates of acquisition are considered consistent. Transformation parameters are computed for the skeleton cloud which could then be applied to the whole UAS dataset. In addition, a separate cloud consisting of non-vegetation features automatically derived using CANUPO classification algorithm (Brodu and Lague, 2012) was used to generate a separate set of parameters. Ground survey is done to validate the transformed cloud. An RMSE value of around 16 centimeters was found when comparing validation data to the models georeferenced using the CANUPO cloud and the manual skeleton cloud. Cloud-to-cloud distance computations of CANUPO and manual skeleton clouds were obtained with values for both equal to around 0.67 meters at 1.73 standard deviation.