Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models

Arce, Samuel; Vernon, Cory A.; Hammond, Joshua; Newell, Valerie; Janson, Joseph; Franke, Kevin W.; Hedengren, John D.

doi:10.3390/rs12132169

Cited by 18 publications

(20 citation statements)

References 48 publications

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“…SfM-based photogrammetry has been used to supplement standard sonar collections by collecting data at shallow depths that sonar technology cannot accurately model [17]; our method extends this research to above-water areas. Others have used sUAV-based methods to monitor changes in coastline due to wave run up [18] and evaluate water resource infrastructures such as spillways [15]. Similar to this study, other studies have also evaluated various parameters on procedures and their effect on accuracy including altitude, ground control, camera angle, and others [5,[32][33][34][35][36].…”

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confidence: 79%

“…Researchers and practitioners have used imagery from sUAVs and Structure from Motion (SfM) algorithms to generate accurate topographical models [15,16]. This is a more cost-effective method for generating topography for small areas compared to traditional aerial photogrammetric methods.…”

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confidence: 99%

“…This is a more cost-effective method for generating topography for small areas compared to traditional aerial photogrammetric methods. Extending these general terrain models, recent studies have demonstrated the use of sUAVs and SfM technology in many environmental resource fields, including the field of water resources [15,[17][18][19][20][21][22][23][24], and other applications [25,26]. The majority of this work revolves around using sUAVs' data collection methods to create accurate three-dimensional (3D) topographic models.…”

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confidence: 99%

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Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

2020

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Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data—both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management.

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confidence: 79%

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confidence: 99%

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Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

2020

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“…Flight planning often uses existing knowledge from elevation data or field measurements to create a rough approximation of the area of interest. Flight paths take into account information, as well as information gaps identified to create a more complete model as shown by Arce et al [4]. The UAV must then be able to fly to locations that "see" all areas of interest from multiple angles.…”

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confidence: 99%

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

et al. 2020

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Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR.

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“…For instance, [40][41][42] proposed on-line next best view planning for UAV with an initial 3D model. Other authors proposed different complete design view planning algorithms for 3D reconstruction of buildings using UAVs [12,21,39,[43][44][45][46][47]. For instance, in [21], the footprint of a building is extracted from DSM generated by nadir UAV imagery.…”

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confidence: 99%

An Imaging Network Design for UGV-Based 3D Reconstruction of Buildings

Ahmadabadian

Remondino

2021

Remote Sensing

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Imaging network design is a crucial step in most image-based 3D reconstruction applications based on Structure from Motion (SfM) and multi-view stereo (MVS) methods. This paper proposes a novel photogrammetric algorithm for imaging network design for building 3D reconstruction purposes. The proposed methodology consists of two main steps: (i) the generation of candidate viewpoints and (ii) the clustering and selection of vantage viewpoints. The first step includes the identification of initial candidate viewpoints, selecting the candidate viewpoints in the optimum range, and defining viewpoint direction stages. In the second step, four challenging approaches—named façade pointing, centre pointing, hybrid, and both centre & façade pointing—are proposed. The entire methodology is implemented and evaluated in both simulation and real-world experiments. In the simulation experiment, a building and its environment are computer-generated in the ROS (Robot Operating System) Gazebo environment and a map is created by using a simulated robot and Gmapping algorithm based on a Simultaneously Localization and Mapping (SLAM) algorithm using a simulated Unmanned Ground Vehicle (UGV). In the real-world experiment, the proposed methodology is evaluated for all four approaches for a real building with two common approaches, called continuous image capturing and continuous image capturing & clustering and selection approaches. The results of both evaluations reveal that the fusion of centre & façade pointing approach is more efficient than all other approaches in terms of both accuracy and completeness criteria.

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Automated 3D Reconstruction Using Optimized View-Planning Algorithms for Iterative Development of Structure-from-Motion Models

Cited by 18 publications

References 48 publications

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

An Imaging Network Design for UGV-Based 3D Reconstruction of Buildings

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