An Improved Scheme for High-Resolution Point Cloud Map Generation Based on FMCW Radar

2020 ◽  
Author(s):  
Qiangwen Zheng ◽  
Yaping Xie ◽  
Lijie Yang ◽  
Junjie Li ◽  
Chunyi Song ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Fmcw Radar ◽  
Map Generation

scholarly journals Geometry Extraction for High Resolution Building Energy Modelling Applications from Point Cloud Data: A Case Study of a Factory Facility

2017 ◽  
Vol 142 ◽  
pp. 1805-1810 ◽  
Author(s):  
Tom Lloyd Garwood ◽  
Ben Richard Hughes ◽  
Dominic O’Connor ◽  
John K Calautit ◽  
Michael R Oates ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Building Energy ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Energy Modelling

scholarly journals AN INTEGRATED PHOTOGRAMMETRIC AND PHOTOCLINOMETRIC APPROACH FOR PIXEL-RESOLUTION 3D MODELLING OF LUNAR SURFACE

2018 ◽  
Vol XLII-3 ◽  
pp. 1117-1122
Author(s):  
W. C. Liu ◽  
B. Wu
Keyword(s):  
High Resolution ◽  
Lunar Surface ◽  
Image Matching ◽  
Point Cloud ◽  
3D Modelling ◽  
Conjugate Points ◽  
Stereo Pair ◽  
Pixel Resolution ◽  
Dense Image ◽  
Lunar Reconnaissance Orbiter

High-resolution 3D modelling of lunar surface is important for lunar scientific research and exploration missions. Photogrammetry is known for 3D mapping and modelling from a pair of stereo images based on dense image matching. However dense matching may fail in poorly textured areas and in situations when the image pair has large illumination differences. As a result, the actual achievable spatial resolution of the 3D model from photogrammetry is limited by the performance of dense image matching. On the other hand, photoclinometry (i.e., shape from shading) is characterised by its ability to recover pixel-wise surface shapes based on image intensity and imaging conditions such as illumination and viewing directions. More robust shape reconstruction through photoclinometry can be achieved by incorporating images acquired under different illumination conditions (i.e., photometric stereo). Introducing photoclinometry into photogrammetric processing can therefore effectively increase the achievable resolution of the mapping result while maintaining its overall accuracy. This research presents an integrated photogrammetric and photoclinometric approach for pixel-resolution 3D modelling of the lunar surface. First, photoclinometry is interacted with stereo image matching to create robust and spatially well distributed dense conjugate points. Then, based on the 3D point cloud derived from photogrammetric processing of the dense conjugate points, photoclinometry is further introduced to derive the 3D positions of the unmatched points and to refine the final point cloud. The approach is able to produce one 3D point for each image pixel within the overlapping area of the stereo pair so that to obtain pixel-resolution 3D models. Experiments using the Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC NAC) images show the superior performances of the approach compared with traditional photogrammetric technique. The results and findings from this research contribute to optimal exploitation of image information for high-resolution 3D modelling of the lunar surface, which is of significance for the advancement of lunar and planetary mapping.

Research on a true digital orthophoto map generation technology based on 3D point cloud geometry

2021 ◽  
Author(s):  
ShuHan Du ◽  
FengJie Zheng ◽  
XiangNing Chen ◽  
Haoyue Wang ◽  
Decheng Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Map Generation ◽  
Digital Orthophoto ◽  
Generation Technology

scholarly journals High-Resolution Representation for Mobile Mapping Data in Curved Regular Grid Model

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5373 ◽  
Author(s):  
Jingxin Su ◽  
Ryuji Miyazaki ◽  
Toru Tamaki ◽  
Kazufumi Kaneda
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Road Surface ◽  
Surface Elevation ◽  
Regular Grid ◽  
Real World Data ◽  
Mobile Mapping ◽  
Cloud Data

As mobile mapping systems become a mature technology, there are many applications for the process of the measured data. One interesting application is the use of driving simulators that can be used to analyze the data of tire vibration or vehicle simulations. In previous research, we presented our proposed method that can create a precise three-dimensional point cloud model of road surface regions and trajectory points. Our data sets were obtained by a vehicle-mounted mobile mapping system (MMS). The collected data were converted into point cloud data and color images. In this paper, we utilize the previous results as input data and present a solution that can generate an elevation grid for building an OpenCRG model. The OpenCRG project was originally developed to describe road surface elevation data, and also defined an open file format. As it can be difficult to generate a regular grid from point cloud directly, the road surface is first divided into straight lines, circular arcs, and and clothoids. Secondly, a non-regular grid which contains the elevation of road surface points is created for each road surface segment. Then, a regular grid is generated by accurately interpolating the elevation values from the non-regular grid. Finally, the curved regular grid (CRG) model files are created based on the above procedures, and can be visualized by OpenCRG tools. The experimental results on real-world data show that the proposed approach provided a very-high-resolution road surface elevation model.

scholarly journals Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

2019 ◽  
Vol 11 (18) ◽  
pp. 2154 ◽  
Author(s):  
Ján Šašak ◽  
Michal Gallay ◽  
Ján Kaňuk ◽  
Jaroslav Hofierka ◽  
Jozef Minár
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Mutual Orientation ◽  
Close Range Photogrammetry ◽  
Combined Use ◽  
Wide Range ◽  
Close Range

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

High-Resolution Large-Area Digital Orthophoto Map Generation Using LROC NAC Images

2019 ◽  
Vol 85 (7) ◽  
pp. 481-491 ◽  
Author(s):  
Kaichang Di ◽  
Mengna Jia ◽  
Xin Xin ◽  
Jia Wang ◽  
Bin Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Area ◽  
Map Generation ◽  
Digital Orthophoto

scholarly journals Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2244
Author(s):  
J. M. Jurado ◽  
J. L. Cárdenas ◽  
C. J. Ogayar ◽  
L. Ortega ◽  
F. R. Feito
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Near Infrared ◽  
Automatic Segmentation ◽  
Hierarchical Cluster ◽  
Semantic Segmentation ◽  
Heterogeneous Data ◽  
Multispectral Images ◽  
Three Dimensional Model ◽  
Natural Materials

The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

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