General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

Kim, Minsu; Park, Seonkyung; Danielson, Jeffrey J.; Irwin, J.; Stensaas, Gregory L.; Stoker, Jason M.; Nimetz, Joshua

doi:10.3390/rs11232737

Cited by 8 publications

(12 citation statements)

References 24 publications

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“…A recent publication by Kim et al [20] proposes a method based on terrestrial laser scanning rather than single GNSS point locations to avoid the influence of point density on airborne lidar data accuracy evaluations. This is accomplished by modeling and intersecting multiple planar surfaces on residential rooftops, using data from both terrestrial (which serves as the reference data of higher accuracy) and airborne lidar point clouds, to produce synthetic conjugate points for comparison.…”

Section: Positional Accuracymentioning

confidence: 99%

Evaluation of SPL100 Single Photon Lidar Data

2020

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Geiger-mode and single photon lidar sensors have recently emerged on the commercial market, advertising greater collection efficiency than the traditional linear mode lidar (LML) systems. Non-linear photon detection is a new technology for the geospatial community, and its performance characteristics for surveying and mapping are not yet well understood. Therefore, the geospatial quality of the data produced by one of these new sensors, the Leica SPL100, is examined by comparing the achieved lidar point cloud accuracy, precision, digital elevation model (DEM) generation, canopy penetration, and multiple return generation to a LML point cloud. We find the SPL100 has a lower ranging precision than linear mode lidar and that the precision is more negatively affected by surface properties such as low intensity and high incidence angle. The accuracy of the SPL100 point cloud, however, was found to be comparable to LML for smooth horizontal surfaces. A 1 m resolution SPL100 DEM was also comparable to a corresponding LML DEM, but the SPL100 was observed to have a reduced ability to resolve multiple returns through vegetation when compared to a LML sensor. In its current state, the SPL100 is likely best suited for applications in which the need for collection efficiency outweighs the need for maximum precision and canopy penetration and modeling.

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Section: Positional Accuracymentioning

confidence: 99%

Evaluation of SPL100 Single Photon Lidar Data

2020

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“…There has been some work on establishing the accuracy of LiDAR-generated point clouds, though Toth, Jozkow, and Grejner-Brzezinska [38] note that with the exception of the Vertical Accuracy Reporting for LiDAR Data (VARLD) guidelines, that there are no generally accepted methods for point cloud accuracy-which, for the surveying methods reported here, is the final product. Kim, Park, Danielson, Irwin, Stensaas, Stoker, and Nimetz [40] state that one method to assess LiDAR accuracy is to compute the error of the vertical component and that assessment of point clouds in full 3D (three dimension) is not routinely performed. Work is continuing on this topic, such as using geometric extensions of man-made structures [41] with new standards being developed [42].…”

Section: Computing Accuracy Differences Between Point Cloud Modelsmentioning

confidence: 99%

“…The CCC tool calculates the Euclidean distance from one point to the nearest neighbor of that point in the other point cloud. This is done for each point in the point clouds and computes the root mean squared error of the distance between the two point clouds in 3D space [40,41]. We used this average distance between the point clouds to quantify the effects of the different investigations we performed.…”

Section: Computing Accuracy Differences Between Point Cloud Modelsmentioning

confidence: 99%

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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“…The pyramid also has a reference point-the apex-which can be both mensurated and surveyed in the field to obtain not only its vertical but also its horizontal position (Wilkinson et al, 2019). Thus a true 3D assessment of the point cloud can be undertaken, which is not often practiced (Kim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Absolute 3d Accuracy Assessment of Uas Lidar Surveying

Lassiter

Wilkinson

Perez

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Surveying an area with small, unoccupied aerial systems (UAS) equipped with a lidar mapping payload—absent permanent, stable, geometrical reference surfaces—demands accurate, repeatable data collection procedures. While relative error within a single UAS lidar dataset may reveal itself in strip misalignment, absolute error (particularly horizontal error) can prove more difficult to detect, casting doubt upon the quality of both individual surveys and time change analyses of multiple surveys of the area. To gain insight on the UAS lidar error budget, this study presents an analysis of multiple UAS lidar surveys over a set of accurately surveyed geometric checkpoints. Each flight’s trajectory was processed multiple times using multiple static GNSS base observations, both autonomous and set over surveyed monuments, at varying distances from the study site. Custom algorithms were used to mensurate the geometric targets detected in each UAS lidar survey's point cloud, allowing for precise comparison of both absolute horizontal and vertical accuracy of each survey against the rigorous ground survey. The results of the analysis suggest that high horizontal accuracy can be achieved under a variety of conditions, whereas vertical accuracy is sensitive to the quality of ground control. and a discussion of the results explores the ultimate goal of isolating and understanding the sources and magnitudes of error in the UAS lidar error budget.

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General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

Cited by 8 publications

References 24 publications

Evaluation of SPL100 Single Photon Lidar Data

Evaluation of SPL100 Single Photon Lidar Data

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

Absolute 3d Accuracy Assessment of Uas Lidar Surveying

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