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HEURISTIC 3D RECONSTRUCTION OF IRREGULAR SPACED LIDAR
Title
HEURISTIC 3D RECONSTRUCTION OF IRREGULAR SPACED LIDAR.
Creator
Shorter, Nicholas, Kasparis, Takis, University of Central Florida
Abstract / Description
As more data sources have become abundantly available, an increased interest in 3D reconstruction has emerged in the image processing academic community. Applications for 3D reconstruction of urban and residential buildings consist of urban planning, network planning for mobile communication, tourism information systems, spatial analysis of air pollution and noise nuisance, microclimate investigations, and Geographical Information Systems (GISs). Previous, classical, 3D reconstruction...
Show moreAs more data sources have become abundantly available, an increased interest in 3D reconstruction has emerged in the image processing academic community. Applications for 3D reconstruction of urban and residential buildings consist of urban planning, network planning for mobile communication, tourism information systems, spatial analysis of air pollution and noise nuisance, microclimate investigations, and Geographical Information Systems (GISs). Previous, classical, 3D reconstruction algorithms solely utilized aerial photography. With the advent of LIDAR systems, current algorithms explore using captured LIDAR data as an additional feasible source of information for 3D reconstruction. Preprocessing techniques are proposed for the development of an autonomous 3D Reconstruction algorithm. The algorithm is designed for autonomously deriving three dimensional models of urban and residential buildings from raw LIDAR data. First, a greedy insertion triangulation algorithm, modified with a proposed noise filtering technique, triangulates the raw LIDAR data. The normal vectors of those triangles are then passed to an unsupervised clustering algorithm – Fuzzy Simplified Adaptive Resonance Theory (Fuzzy SART). Fuzzy SART returns a rough grouping of coplanar triangles. A proposed multiple regression algorithm then further refines the coplanar grouping by further removing outliers and deriving an improved planar segmentation of the raw LIDAR data. Finally, further refinement is achieved by calculating the intersection of the best fit roof planes and moving nearby points close to that intersection to exist at the intersection, resulting in straight roof ridges. The end result of the aforementioned techniques culminates in a well defined model approximating the considered building depicted by the LIDAR data.
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Date Issued
2006
Identifier
CFE0001315, ucf:47017
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001315
UNSUPERVISED BUILDING DETECTION FROM IRREGULARLY SPACED LIDAR AND AERIAL IMAGERY
Title
UNSUPERVISED BUILDING DETECTION FROM IRREGULARLY SPACED LIDAR AND AERIAL IMAGERY.
Creator
Shorter, Nicholas, Kasparis, Takis, University of Central Florida
Abstract / Description
As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile...
Show moreAs more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and aerial imagery are available, then the algorithm will use them both for improved accuracy. Additionally, the proposed approach does not employ severely limiting assumptions thus enabling the end user to apply the approach to a wider variety of different building types. The proposed approach is extensively tested using real data sets and it is also compared with other existing techniques. Experimental results are presented.
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Date Issued
2009
Identifier
CFE0002783, ucf:48125
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002783
A Triangulation Based Coverage Path Planning For a Mobile Robot With Circular Sensing Range
Title
A Triangulation Based Coverage Path Planning For a Mobile Robot With Circular Sensing Range.
Creator
An, Vatana, Qu, Zhihua, Haralambous, Michael, Mikhael, Wasfy, University of Central Florida
Abstract / Description
In this dissertation, two coverage path planning (CPP) approaches for a nonholonomic mobile robot are proposed. The first approach is the Local Coverage Path Planning (LCPP) approach which is designed for all sensing ranges. The second approach is the Global Coverage Path Planning (GCPP) approach which is designed for sufficient sensing range that can observe all points of interests in the target region (TR). The LCPP approach constructs CP after finding observer points for all local regions...
Show moreIn this dissertation, two coverage path planning (CPP) approaches for a nonholonomic mobile robot are proposed. The first approach is the Local Coverage Path Planning (LCPP) approach which is designed for all sensing ranges. The second approach is the Global Coverage Path Planning (GCPP) approach which is designed for sufficient sensing range that can observe all points of interests in the target region (TR). The LCPP approach constructs CP after finding observer points for all local regions in the TR. The GCPP approach computes observer points after CP construction. Beginning with the sample TR, the LCPP approach requires 8 algorithms to find a smooth CP and sufficient number of observers for complete coverage. The Global Coverage Path Planning approach requires 17 algorithms to find the smooth CP with sufficient number of observers for completed coverage. The worst case running time for both approaches are quadratic which is consider to be very fast as compared to previous works reported in the literature. The main technical contributions of both approaches are to provide a holistic solution that segments any TR, uses triangulation to determine the line of sights and observation points, and then compute the smooth and collision-free CP. Both approaches provide localization, speed control, curvature control, CP length control, and smooth CP control. The first approach has applications in automate vacuum cleaning, search and rescue mission, spray painting, and etc. The second approach is best used in military and space applications as it requires infinite sensing range which only resource rich organizations can afford. At the very least, the second approach provides simulation opportunity and upper bound cost estimate for CPP. Both approaches will lead to a search strategy that provides the shortest CP with the minimum number of observer and with the shortest running time for any sensing range.
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Date Issued
2017
Identifier
CFE0006853, ucf:51745
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006853