Computing highly occluded paths on a terrain

Lebeck, Niel; Mølhave, Thomas; Agarwal, Pankaj K.

doi:10.1145/2525314.2525363

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…There are many interesting compute-intensive terrain analysis problems defined on GRID DEMs that could lend themselves to GPU computations. Some work has already been done in that direction (e.g., Lebeck et al [2013]), but many more problems can benefit from the parallelism offered by a GPU. We are particularly interested in problems involving topological persistence [Edelsbrunner et al 2000] and hydrological analysis [Danner et al 2007].…”

Section: Resultsmentioning

confidence: 99%

TerraNNI

Agarwal

Beutel

Mølhave

2016

ACM Trans. Spatial Algorithms Syst.

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With modern focus on remote sensing technology, such as LiDAR, the amount of spatial data, in the form of massive point clouds, has increased dramatically. Furthermore, repeated surveys of the same areas are becoming more common. This trend will only increase as topographic changes prompt surveys over already scanned areas, in which case we obtain large spatiotemporal datasets. An initial step in the analysis of such spatial data is to create a digital elevation model representing the terrain, possibly over time. In the case of spatial (spatiotemporal, respectively) datasets, these models often represent elevation on a 2D (3D, respectively) grid. This involves interpolating the elevation of LiDAR points on these grid points. In this article, we show how to efficiently perform natural neighbor interpolation over a 2D and 3D grid. Using a graphics processing unit (GPU), we describe different algorithms to attain speed and GPU-memory tradeoffs. Our experimental results demonstrate that our algorithms not only are significantly faster than earlier ones but also scale to much bigger datasets that previous algorithms were unable to handle.

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Section: Resultsmentioning

confidence: 99%

TerraNNI

Agarwal

Beutel

Mølhave

2016

ACM Trans. Spatial Algorithms Syst.

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“…On multiple observer siting, Lebeck et al [7] proposed an algorithm for computing highly occluded paths on a terrain with unknown observer positions. They define the path cost as the sum of point visibility along a path and compute the minimum cost path using Dijkstra's algorithm.…”

Section: Related Workmentioning

confidence: 99%

Parallel multiple observer siting on terrain

Franklin

Benedetti

et al. 2014

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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This paper presents the optimization and parallelization of the multiple observer siting program, originally developed by Franklin and Vogt. Siting is a compute-intensive application with a large amount of inherent parallelism. The advantage of parallelization is not only a faster program but also the ability to solve bigger problems. We have parallelized the program using two different techniques: OpenMP, using multi-core CPUs, and CUDA, using a general purpose graphics processing unit (GPGPU). Experiment results show that both techniques are very effective. Using the OpenMP program, we are able to site tens of thousands of observers on a 16385 × 16385 terrain in less than 2 minutes, on our workstation with two CPUs and one GPU. The CUDA program achieves the same in about 30 seconds.

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“…Several other bicriteria measures have been proposed in the context of path planning amid obstacles in R 2 , which combine the length of the path with curvature, the number of links in the path, the visibility of the path, and so on (see, e.g., References [1,5,14] and references therein).…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Computing High-Quality Paths amid Polygonal Obstacles

Agarwal

Fox

Salzman

2018

ACM Trans. Algorithms

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We study a path-planning problem amid a set O of obstacles in R 2 , in which we wish to compute a short path between two points while also maintaining a high clearance from O; the clearance of a point is its distance from a nearest obstacle in O. Specifically, the problem asks for a path minimizing the reciprocal of the clearance integrated over the length of the path. We present the first polynomial-time approximation scheme for this problem. Let n be the total number of obstacle vertices and let ε ∈ (0, 1]. Our algorithm computes in time O (n 2 ε 2 log n ε) a path of total cost at most (1 + ε) times the cost of the optimal path. CCS Concepts: • Theory of computation → Approximation algorithms analysis; Computational geometry; • Computing methodologies → Robotic planning;

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Computing highly occluded paths on a terrain

Cited by 7 publications

References 27 publications

TerraNNI

TerraNNI

Parallel multiple observer siting on terrain

An Efficient Algorithm for Computing High-Quality Paths amid Polygonal Obstacles

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