Soohee Han

Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid

In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS) data. The method creates a raster digital surface model (DSM) by interpolating point data with inverse distance weighting (IDW), and produces a digital terrain model (DTM) by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.

Automated and Efficient Method for Extraction of Tunnel Cross Sections Using Terrestrial Laser Scanned Data

An automated and efficient method for extraction of tunnel cross sections using terrestrial laser scanner (TLS) data is presented. In this process, a three-dimensional (3D) point cloud acquired from the TLS is projected onto a horizon plane, converted to a two-dimensional (2D) planar image, and skeletonized to estimate the tunnel centerline. Stations from which cross sections are extracted are estimated from that centerline, which is vectorized and smoothed for better estimation. From those stations, cross-sectional planes are estimated, and point groups surrounding the planes are extracted. By using these point groups, the azimuth angles of the cross-sectional planes are adjusted, and the final cross sections are generated by projecting the nearby points to the adjusted planes. The performance of the proposed method was evaluated by applying it to a real tunnel and comparing the results with those from a conventional method that uses a total station. In the results, the cross sections were extracted at the stations corresponding to the conventional method. The proposed method proved itself to offer advantages, including detailed description and improved surveying and data processing efficiencies.

Development of a hashing-based data structure for the fast retrieval of 3D terrestrial laser scanned data

The volume of point cloud data obtained by 3-dimensional terrestrial laser scanners has grown very large as a result of scanner enhancements and application extensions. Quick point querying is therefore essential for efficient point cloud processing, and several data structures are applicable for that purpose. Octree, for example, is utilized in similar approaches and is considered a good candidate. This paper introduces hashing-based virtual grid (HVG), both as a competitor for octree and an improvement on the 3-dimensional virtual grid (3DVG). Whereas 3DVG is defined as a 3-dimensional array, HVG substitutes hashes for 3DVGs vertical indices. The performance of HVG was evaluated against those of octree and 3DVG by a point-querying operation. The selected operation finds neighboring points residing within a given radius for every individual point in the point cloud. HVG proved its balancing aspects throughout the operation, showing reasonable performance and memory efficiency. 3DVG, while its performance was excellent, required a significantly larger amount of memory. In summary, HVG is a suitable alternative to octree, and is expected to be effectively utilized as a base data structure for any application dealing with a massive amount of 3-dimensional point cloud data.

Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm

Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN) algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.

Infiltration route analysis using thermal observation devices (TOD) and optimization techniques in a GIS environment.

Infiltration-route analysis is a military application of geospatial information system (GIS) technology. In order to find susceptible routes, optimal-path-searching algorithms are applied to minimize the cost function, which is the summed result of detection probability. The cost function was determined according to the thermal observation device (TOD) detection probability, the viewshed analysis results, and two feature layers extracted from the vector product interim terrain data. The detection probability is computed and recorded for an individual cell (50 m × 50 m), and the optimal infiltration routes are determined with A* algorithm by minimizing the summed costs on the routes from a start point to an end point. In the present study, in order to simulate the dynamic nature of a real-world problem, one thousand cost surfaces in the GIS environment were generated with randomly located TODs and randomly selected infiltration start points. Accordingly, one thousand sets of vulnerable routes for infiltration purposes could be found, which could be accumulated and presented as an infiltration vulnerability map. This application can be further utilized for both optimal infiltration routing and surveillance network design. Indeed, dynamic simulation in the GIS environment is considered to be a powerful and practical solution for optimization problems. A similar approach can be applied to the dynamic optimal routing for civil infrastructure, which requires consideration of terrain-related constraints and cost functions.

A fast and automated method for extracting tunnel cross-sections using terrestrial laser scanned data

In tunnel construction, overcut along with undercut estimation is one of the most important factors to be considered before proceeding to the next operation. It is currently analyzed based on sparsely sampled points surveyed using a total station, but not much time is allowed for surveying and analyzing for economic reasons. A fast and automated method is presented to extract dense tunnel cross-sections using Terrestrial Laser Scanner (TLS) data. A 3D point cloud acquired from the TLS is converted to a two-dimensional planar image and skeletonized to estimate the tunnel centerline. Cross-sections are extracted orthogonal to the centerline. To evaluate the performance of the proposed method, it was applied to actual tunnel data and compared with the results from a conventional method using a total station. In the results, the cross-sections were extracted at center points corresponding to those of the conventional method. The proposed method proved itself to have advantages in terms of its ability to offer a detailed description and improve the efficiency of the processing time.