Cleaning massive sonar point clouds

Arge, Lars; Larsen, Kasper Green; Mølhave, Thomas; Walderveen, Freek van

doi:10.1145/1869790.1869815

Cited by 15 publications

(23 citation statements)

References 13 publications

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“…Similar studies, examining the influence of TIN (triangulated irregular network) on the accuracy of created models, were described in publication (Agugiaro and Kolbe 2012;Isenburg et al 2006;Arge et al 2010). However, for large data sets it seems more justified to use grid (Brasington and Richards 1998;Falcao et al 2013;Jalving 1999;Luo et al 2014;Yanalak 2003).…”

Section: Introductionmentioning

confidence: 87%

The influence of the grid resolution on the accuracy of the digital terrain model used in seabed modeling

Maleika

2014

Mar Geophys Res

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Modern digital terrain models (DTM) are widely used in the exploration of water areas. The models are often based on bathymetric data from a multibeam echosounder. DTM creators should properly select model parameters, firstly the grid resolution. High grid resolution enables creating very accurate models, however, they require high computing power and the data gathered in the grid occupy much more memory space. Low grid resolution means significantly less data describing the model, but, naturally, its accuracy will also be lower. The author proposes a method permitting to examine the accuracies of DTMs that depend on adopted grid resolution. Further the article will present search for an accurate grid resolution for three selected real surfaces of the seabed. The obtained results were visualized and interpreted. The author also proposes tips to be used while creating DTMs. Conclusions from the research may be helpful in digital terrain modeling of the seabed.

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Section: Introductionmentioning

confidence: 87%

The influence of the grid resolution on the accuracy of the digital terrain model used in seabed modeling

Maleika

2014

Mar Geophys Res

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“…To construct G, we find the graph vertices by computing connected components. This can be done in O(scan(N )) I/Os using existing I/O-efficient algorithms [11,12]. We then perform a scan to construct the graph edges.…”

Section: A Appendix: Computing Candidate Cellsmentioning

confidence: 99%

Computing Floods Caused by Non-Uniform Sea-Level Rise

Arge

Shin²,

Tsirogiannis

2018

2018 Proceedings of the Twentieth Workshop on Algorithm Engineering and Experiments (ALENEX)

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Predicting floods caused by the rise of the sea level is a critical task for preventing large scale catastrophes. Such predictions can potentially be made using a forecast of the sea level and a detailed model of the terrain. However, since available terrain datasets can easily exceed the size of the main memory of a standard computer, I/O (rather than internal computation time) can often become the bottleneck when computing such predictions. Thus to perform predictions efficiently we need an I/O-efficient approach, which minimizes the transfer of data blocks between main memory and disk. Given a terrain raster T and a sea-level forecast raster S of N cells each, we examine the problem of computing the water level of the induced flood for each cell in T . We introduce an I/O-efficient algorithm for this problem that uses O((N/B) log M/B (X/B)) I/Os after O((N/B) log M/B (N/B)) I/Os of preprocessing, where X is the number of local minima in T , and M and B are the size of main memory and data block, respectively. When X < M (which holds in practice) our algorithm requires optimal O(N/B) I/Os after preprocessing. We have implemented our algorithm and put considerable effort into engineering it. We present experiments that illustrate the efficiency and practicality of the algorithm, which is so efficient that work is underway to incorporate our results in the forecast services of the Danish Meteorological Institute.

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“…The reflection strength may also be recorded and, thus, enrich the feature vector [31]. Point cloud processing of large MBES (Multi-beam Echo Sounding) datasets is described, e.g., in Arge et al [32]. The case of MBES point clouds will not be discussed further.…”

Section: Acquisition Of Point Cloudsmentioning

confidence: 99%

“…In certain situations, even linear structures, such as a simple tiling space partition [81] or lexicographically sorting by point coordinates [32], may be satisfactory.…”

Section: Data Structures and Spatial Indicesmentioning

confidence: 99%

Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

Otepka

Ghuffar

Waldhauser

et al. 2013

IJGI

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This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features.

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Cleaning massive sonar point clouds

Cited by 15 publications

References 13 publications

The influence of the grid resolution on the accuracy of the digital terrain model used in seabed modeling

The influence of the grid resolution on the accuracy of the digital terrain model used in seabed modeling

Computing Floods Caused by Non-Uniform Sea-Level Rise

Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

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