Combining Interior Orientation Variables to Predict the Accuracy of Rpas–Sfm 3D Models

Abstract

Remotely piloted aerial systems (RPAS) have been recognized as an effective low-cost tool to acquire photogrammetric data of low accessible areas reducing collection and processing time. Data processing techniques like structure from motion (SfM) and multiview stereo (MVS) techniques, can nowadays provide detailed 3D models with an accuracy comparable to the one generated by other conventional approaches. Accuracy of RPAS-based measures is strongly dependent on the type of adopted sensors. Nevertheless, up to now, no investigation was done about relationships between camera calibration parameters and final accuracy of measures. In this work, authors tried to fill this gap by exploring those dependencies with the aim of proposing a prediction function able to quantify the potential final error in respect of camera parameters. Predictive functions were estimated by combining multivariate and linear statistical techniques. Four photogrammetric RPAS acquisitions were considered, supported by ground surveys, to calibrate the predictive model while a further acquisition was used to test and validate it. Results are preliminary, but promising. The calibrated predictive functions relating camera internal orientation (I.O.) parameters with final accuracy of measures (root mean squared error) showed high reliability and accuracy.

Influence of Co-Alignment Procedures on the Co-Registration Accuracy of Multi-Epoch SFM Points Clouds

“…Conversely, by comparing the percentage changes between RMSEP and RMSE3D, it is evident that planar components affect more the final error than the vertical one since RMSE3D is slightly higher in all examined scenarios. Nevertheless, georeferencing strategies influence their results since their values improves with the increment of the number of GCPs, as already discussed by (Capolupo et al 2020a;Saponaro et al, 2019b). RMSE3D)) measured on the 10 CPs distributed in the investigated scene.…”

“…Moreover, a low-cost GNSS/INS positioning receiver, set to record RPAS geographical coordinates in WGS84 (EPSG: 4326) reference system, and a barometer, aimed at storing the flight altitude, were used as well. The flight plan was programmed to obtain an average Ground Sampling Distance (GSD) of about 0.04 m/pix, a forward and side overlaps of 85% and 75%, respectively, by applying DJI Ground Station Pro application (Capolupo et al, 2020a). This ensured to perform all field data campaigns using the same path, waypoints and flight conditions (e.g., cruising speed of 4.0 m/s and flight height of 100 m Above Ground Level (AGL)).…”

“…Without a purposed calibration of the parameters of the camera and its lever arm, errors might be even more significant. While a self-calibration procedure is commonly preferred, this can also be arranged through rigorous operations in laboratory (Capolupo et al, 2020a;Saponaro et al, 2020d). The Agisoft Metashape software uses Brown's 12component lens representation model (Fryer, Brown, 1986): in this work the radial and tangential distortion parameters above the third order (K3, K4, P3, P4, respectively) were not accounted in order to avoid overparameterisation issues (James et al, 2017).…”

“…This command adjusts and refines the scene geometries and camera calibration (if authorized) while minimizing squared redesign errors between image and photogrammetric block points. The software aggregates heterogeneous information useful for BBA compensations: GCPs implemented in the block are fundamental for their accuracy (Saponaro et al 2019c;Capolupo et al, 2020a). In the case of DG, the position information recorded in each image by the RPAS receiver were useful, even if they transferred less sizeable corrections due to their lower accuracy.…”

Combining UAV-Based SfM-MVS Photogrammetry with Conventional Monitoring to Set Environmental Flows: Modifying Dam Flushing Flows to Improve Alpine Stream Habitat

“…Long-established lessons regarding how to calibrate nonmetric cameras in analog and analytical photogrammetry (e.g., [141,142]) still apply. A number of studies have sought to provide guidance on how to optimize SfM-MVS image data collection and processing (e.g., [143][144][145][146]); however, it is only recently that there has been the controlled investigation of what determines the internal geometry of cameras used in a UAV-based SfM-MVS photogrammetric framework (e.g., [101,147]). There is growing evidence that such guidance cannot be generalized either between UAV systems or between environments using the same UAV system [101].…”