Methods of laser scanning point clouds integration in precise 3D building modelling

Kędzierski, Michał; Fryśkowska, Anna

doi:10.1016/j.measurement.2015.07.015

Cited by 55 publications

(33 citation statements)

References 21 publications

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“…In post-processing step very important is classification accuracy or quality of denoising of the point cloud. The accuracy of acquiring and post processing laser scanning data is briefly described in many publications, and briefly described in (Fryskowska, 2017a), (Kedzierski and Fryskowska, 2015), (Przyborski, 2003). Below we propose a two-way point cloud processing scheme in fully automatic way.…”

Section: Methodsmentioning

confidence: 99%

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

Fryśkowska¹,

Kędzierski²,

Walczykowski³

et al. 2017

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

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ABSTRACT:The archaeological heritage is non-renewable, and any invasive research or other actions leading to the intervention of mechanical or chemical into the ground lead to the destruction of the archaeological site in whole or in part. For this reason, modern archeology is looking for alternative methods of non-destructive and non-invasive methods of new objects identification. The concept of aerial archeology is relation between the presence of the archaeological site in the particular localization, and the phenomena that in the same place can be observed on the terrain surface form airborne platform. One of the most appreciated, moreover, extremely precise, methods of such measurements is airborne laser scanning. In research airborne laser scanning point cloud with a density of 5 points/sq. m was used. Additionally unmanned aerial vehicle imagery data was acquired. Test area is located in central Europe. The preliminary verification of potentially microstructures localization was the creation of digital terrain and surface models. These models gave an information about the differences in elevation, as well as regular shapes and sizes that can be related to the former settlement/sub-surface feature. The paper presents the results of the detection of potentially sub-surface microstructure fields in the forestry area.

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Section: Methodsmentioning

confidence: 99%

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

Fryśkowska¹,

Kędzierski²,

Walczykowski³

et al. 2017

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

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“…Those parallel rectangles range from the bottom to the top, and the four corresponding corners of all rectangles are collinear. For each bin, a convex hull algorithm [30] and a polygon simplification algorithm are used to extract corner points [22,23]. As principal supporting structures, the size of four principal legs are larger than that of other auxiliary components, leading to the number of points falling on the four principal legs of the pylon body being greater than that of auxiliary components; correspondingly, the color of the four principal legs are darker than other auxiliary components in Figure 4a.…”

Section: Extraction and Segmentation Of Corner Pointsmentioning

confidence: 99%

Section: Extraction and Segmentation Of Corner Pointsmentioning

confidence: 99%

A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data

et al. 2017

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Abstract:Object reconstruction from airborne LiDAR data is a hot topic in photogrammetry and remote sensing. Power fundamental infrastructure monitoring plays a vital role in power transmission safety. This paper proposes a heuristic reconstruction method for power pylons widely used in high voltage transmission systems from airborne LiDAR point cloud, which combines both data-driven and model-driven strategies. Structurally, a power pylon can be decomposed into two parts: the pylon body and head. The reconstruction procedure assembles two parts sequentially: firstly, the pylon body is reconstructed by a data-driven strategy, where a RANSAC-based algorithm is adopted to fit four principal legs; secondly, a model-driven strategy is used to reconstruct the pylon head with the aid of a predefined 3D head model library, where the pylon head's type is recognized by a shape context algorithm, and their parameters are estimated by a Metropolis-Hastings sampler coupled with a Simulated annealing algorithm. The proposed method has two advantages: (1) optimal strategies are adopted to reconstruct different pylon parts, which are robust to noise and partially missing data; and (2) both the number of parameters and their search space are greatly reduced when estimating the head model's parameters, as the body reconstruction results information about the original point cloud, and relationships between parameters are used in the pylon head reconstruction process. Experimental results show that the proposed method can efficiently reconstruct power pylons, and the average residual between the reconstructed models and the raw data was smaller than 0.3 m.

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“…For example, MLS systems typically experience a reduction in positioning accuracy in poor GNSS visibility, such as dense urban environments and forest [3]. Co-registration by data becomes increasingly difficult if the viewpoints have less overlap [96]. Not only can data from several platforms be co-registered [97], but ALS can be used to remedy some positioning accuracy issues of overlapping MLS [49].…”

Section: Multi-sensor Integrationmentioning

confidence: 99%

Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

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Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-information products, such as digital terrain and elevation models and 3D building models, are produced automatically from these data. We outline the data acquisition, processing, and application of the point cloud. As a national data set, a dense multispectral point cloud could produce significant cost savings via improved automation in mapping and a reduction of overlapping surveying efforts.

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Methods of laser scanning point clouds integration in precise 3D building modelling

Cited by 55 publications

References 21 publications

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

Effective Detection of Sub-Surface Archeological Features From Laser Scanning Point Clouds and Imagery Data

A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data

Nationwide Point Cloud—The Future Topographic Core Data

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