Open Source and Independent Methods for Bundle Adjustment Assessment in Close-Range UAV Photogrammetry

Abstract

Close-range photogrammetry as a technique to acquire reality-based 3D data has, in recent times, seen a renewed interest due to developments in sensor technologies. Furthermore, the strong democratization of UAVs (Unmanned Aerial Vehicles) or drones means that close-range photogrammetry can now be used as a viable low-cost method for 3D mapping. In terms of software development, this led to the creation of many commercial black-box solutions (PhotoScan, Pix4D, etc.). This paper aims to demonstrate how the open source toolbox DBAT (Damped Bundle Adjustment Toolbox) can be used to generate detailed photogrammetric network diagnostics to help assess the quality of surveys processed by the commercial software, PhotoScan. In addition, the Apero module from the MicMac software suite was also used to provide an independent assessment of the results. The assessment is performed by the careful examination of some of the bundle adjustment metrics generated by both open source solutions. A UAV project was conducted on a historical church in the city center of Strasbourg, France, in order to provide a dataset with a millimetric level of precision. Results showed that DBAT can be used to reprocess PhotoScan projects under similar conditions and derive useful metrics from them, while Apero provides a completely independent verification of the results of commercial solutions. Overall, this paper shows that an objective assessment of photogrammetric results is important. In cases where problems are encountered in the project, this assessment method can be useful to detect errors that may not be explicitly presented by PhotoScan.

Investigations on the Bundle Adjustment Results From SFM-Based Software for Mapping Purposes

“…This will be done by comparing the bundle adjustment results on the same dataset processed in Trimble Inpho and ERDAS IMAGINE Photogrammetry, two photogrammetric programmes which have been used extensively for mapping purposes. Similar assessments to PhotoScan's quality can be found in the literature, for example in the case of close-range photogrammetry (Murtiyoso et al, 2018) or aerial UAV photogrammetry (James et al, 2017;Ouédraogo et al, 2014).…”

“…For example, comparisons has been done in terms of dense point cloud (Murtiyoso and Grussenmeyer, 2017;Sapirstein, 2016), Digital Surface Models (DSM) (Remondino et al, 2013;Runkel et al, 2017), bundle adjustment results (Murtiyoso et al, 2018), or orthophoto generation (Chiabrando et al, 2015). This paper will focus on bundle adjustment or aerotriangulation assessment, being one of the most important factors influencing the final precision of topographic mapping.…”

Bundle Adjustment Accuracy Assessment of Unordered Aerial Dataset Collected Through Kite Platform

“…The only way to get an accurate estimate of precision for the object points is hence to look at the a-posteriori standard errors from the BA process. For this purpose, the open source toolbox DBAT (Damped Bundle Adjustment Toolbox) for Matlab has been used to generate detailed photogrammetric network diagnostics to help assess the quality of the surveys computed initially in commercial software (Börlin and Grussenmeyer 2016;Murtiyoso et al, 2018). The assessment was performed by the careful examination of some of the BA metrics.…”

3d Modeling of Roman Bridge by the Integration of Terrestrial and Uav Photogrammetric Survey for Structural Analysis Purpose

“…Generally, the workflow that allows to produce 3D reconstructions/3D models in most common photogrammetry software may be synthesized as (Rahaman & Champion, 2019): (i) Image acquisition (or adding photos), (ii) Feature detection, matching, triangulation (or align photos) (iii) Sparse reconstruction, bundle adjustment (or point cloud generation) (iv) Dense correspondence matching (or dense cloud generation) (v) Mesh/surface generation, and (vi) Texture generation. Recently, the combination of SfM algorithms (Remondino, 2013;Angelini et al, 2017;Murtiyoso et al, 2018) and aerial images obtained by UAV (Unmanned Aerial Vehicle) platform has revolutionized photogrammetric surveying, allowing obtaining 3D models through low-cost, fast and high quality data acquisition and processing (Cucchiaro et al, 2018). In addition, the use of UAV is really useful in critical scenarios, where the easy transportability and low cost of the necessary equipment for aerial survey compared to laser scanning techniques represent a great advantage.…”