scholarly journals Identification of shorelines for natural watercourses with the use of point cloud

2018 ◽  
Vol 71 ◽  
pp. 00014
Author(s):  
Ewa Sudoł
Keyword(s):  
Real Estate ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Actual State ◽  
Water Law ◽  
Boundary Line ◽  
Technical Standards ◽  
Applicable Law ◽  
Vertical Sections

The article presents the method of identifying the shorelines for natural watercourses located in agricultural and forest areas in accordance with applicable law. In the process of developing methods for identification and verification of the actual course of watercourses, data from the cadastral map was used in the form of a vector drawing of borders and a database with border points in ZRD, BPP attributes, metadata and point clouds. The identification of the course of a watercourse on shrubbery and wooded areas as well as on-screen vectorization of the shoreline is cumbersome, and in some cases even impossible. In connection with the above, it has been proposed to use a point cloud and vertical sections prepared on their basis that run perpendicular to the edge of the watercourse. On their basis, the course of the shoreline was recognized in accordance with the definition contained in the Act on Water Law. Pursuant to § 9 para. 3a, beginning of the regulation that the land occupied by the natural seepage constitutes a separate cadastral plot within the boundary line, the suggested procedures for verifying the boundaries of watercourses can be used to update the land and building register databases. The identification of the boundaries of registered parcels made on the principles described in the publication may precede the activities of accepting the boundaries to the division of real estate. On the other hand, the course of the identified, in the mode of § 82a, the regulation the boundaries of registration plots constituting natural watercourses can be shown in the land and building register on the terms specified in art. 24 sec. 2b point 2) geodesy law, in order to replace data inconsistent with the actual state and applicable technical standards, respectively, data consistent with the actual state and applicable technical standards (§ 45 section 1 point 1 of the Regulation).

scholarly journals Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads

2020 ◽  
Vol 12 (10) ◽  
pp. 1677 ◽  
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquin Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
Henrique Lorenzo
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Applicable Law ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Preventive Actions ◽  
Total Study Area ◽  
Processing Techniques ◽  
Flammable Material

The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.

scholarly journals Iterative K-Closest Point Algorithms for Colored Point Cloud Registration

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5331
Author(s):  
Ouk Choi ◽  
Min-Gyu Park ◽  
Youngbae Hwang
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Iterative Closest Point ◽  
Depth Images ◽  
The Cost ◽  
Soft Matching ◽  
Colored Point ◽  
Refinement Algorithm

We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.

A new flexible laser beam profiler for the inspection of train wheels

2020 ◽  
pp. 095440972092200
Author(s):  
M Torabi ◽  
SM Mousavi G ◽  
D Younesian
Keyword(s):  
Laser Beam ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Calibration Method ◽  
The Other ◽  
Geometrical Parameters ◽  
Prototype System ◽  
Perpendicular Plane ◽  
Wheel Profile

In this paper, a flexible laser beam profiler is proposed to easily measure the profile of a train wheel for railway inspection. It only requires two laser beams (together and in parallel) to obtain two three-dimensional point-clouds based on the laser triangulation principle. Either the laser beam profiler or the wheel can be freely moved. The motion need not be known. The wheel profile will be obtained in two steps. First, the wheel axis position and orientation are obtained by minimizing the distance between one of the point-clouds and the other translated point-cloud, and translation is defined as a rotation of any point on the point-cloud around the wheel axis until it lies on the other point-cloud's laser plane. In the second step, the wheel profile is extracted by selecting one of the point-clouds and rotating it about the wheel axis and by finding the intersection of rotating points and a perpendicular plane, the perpendicular plane is defined as any arbitrary plane which passes through the wheel axis. This method is useful particularly for obtaining geometrical parameters of a wheel such as flange height, flange slope and flange thickness. In order to commission the proposed method, a prototype system was designed and manufactured. The performance of the system, evaluated in different circumstances, shows a measurement error of up to 2%. Compared with classical methods utilizing a caliper or those which use expensive equipment or additional parts such as reference guides, the proposed method is easy to use and flexible. Also, a novel calibration method is utilized to calibrate the system accurately and freely.

scholarly journals The Use of UAV in Cadastral Mapping of the Czech Republic

2021 ◽  
Vol 10 (6) ◽  
pp. 380
Author(s):  
Václav Šafář ◽  
Markéta Potůčková ◽  
Jakub Karas ◽  
Jan Tlustý ◽  
Eva Štefanová ◽  
...  
Keyword(s):  
Czech Republic ◽  
Real Estate ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Maximum Efficiency ◽  
Image Point ◽  
The Czech Republic ◽  
Main Challenge ◽  
Check Points

The main challenge in the renewal and updating of the Cadastre of Real Estate of the Czech Republic is to achieve maximum efficiency but to retain the required accuracy of all points in the register. The paper discusses the possibility of using UAV photogrammetry and laser scanning for cadastral mapping in the Czech Republic. Point clouds from images and laser scans together with orthoimages were derived over twelve test areas. Control and check points were measured using geodetic methods (RTK-GNSS and total stations). The accuracy of the detailed survey based on UAV technologies was checked on hundreds of points, mainly building corners and fence foundations. The results show that the required accuracy of 0.14 m was achieved on more than 80% and 98% of points in the case of the image point clouds and orthoimages and the case of the LiDAR point cloud, respectively. Nevertheless, the methods lack completeness of the performed survey that must be supplied by geodetic measurements. The paper also provides a comparison of the costs connected to traditional and UAV-based cadastral mapping, and it addresses the necessary changes in the organisational and technological processes in order to utilise the UAV based technologies.

scholarly journals Comparision of photogrammetric point clouds with BIM building elements for construction progress monitoring

2014 ◽  
Vol XL-3 ◽  
pp. 341-345 ◽  
Author(s):  
S. Tuttas ◽  
A. Braun ◽  
A. Borrmann ◽  
U. Stilla
Keyword(s):  
Point Cloud ◽  
Progress Monitoring ◽  
Point Clouds ◽  
Actual State ◽  
Existing Building ◽  
Building Information Model ◽  
Building Information ◽  
City Construction ◽  
Construction Progress Monitoring ◽  
Construction Schedule

For construction progress monitoring a planned state of the construction at a certain time (as-planed) has to be compared to the actual state (as-built). The as-planed state is derived from a building information model (BIM), which contains the geometry of the building and the construction schedule. In this paper we introduce an approach for the generation of an as-built point cloud by photogrammetry. It is regarded that that images on a construction cannot be taken from everywhere it seems to be necessary. Because of this we use a combination of structure from motion process together with control points to create a scaled point cloud in a consistent coordinate system. Subsequently this point cloud is used for an as-built – as-planed comparison. For that voxels of an octree are marked as occupied, free or unknown by raycasting based on the triangulated points and the camera positions. This allows to identify not existing building parts. For the verification of the existence of building parts a second test based on the points in front and behind the as-planed model planes is performed. The proposed procedure is tested based on an inner city construction site under real conditions.

scholarly journals Interactive Correspondence Selection

2011 ◽  
Author(s):  
David Doria
Keyword(s):  
Fundamental Matrix ◽  
Point Cloud ◽  
Point Clouds ◽  
The Other ◽  
Data Sets ◽  
Data Set

This document presents a GUI application to manually select corresponding points in two data sets. The data sets can each be either an image or a point cloud. If both data sets are images, the functionality is equivalent to Matlab’s ‘cpselect’ function. There are many uses of selecting correspondences. If both data sets are images, the correspondences can be used to compute the fundamental matrix, or to perform registration. If both data sets are point clouds, the correspondences can be used to compute a landmark transformation. If one data set is an image and the other is a point cloud, the camera matrix relating the two can be computed.

An efficient simplification method for point cloud based on salient regions detection

2019 ◽  
Vol 53 (2) ◽  
pp. 487-504 ◽  
Author(s):  
Abdul Rahman El Sayed ◽  
Abdallah El Chakik ◽  
Hassan Alabboud ◽  
Adnan Yassine
Keyword(s):  
Point Cloud ◽  
Processing Time ◽  
Point Clouds ◽  
The Other ◽  
Point Cloud Data ◽  
Weighted Graphs ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Salient Regions ◽  
The Stability

Many computer vision approaches for point clouds processing consider 3D simplification as an important preprocessing phase. On the other hand, the big amount of point cloud data that describe a 3D object require excessively a large storage and long processing time. In this paper, we present an efficient simplification method for 3D point clouds using weighted graphs representation that optimizes the point clouds and maintain the characteristics of the initial data. This method detects the features regions that describe the geometry of the surface. These features regions are detected using the saliency degree of vertices. Then, we define features points in each feature region and remove redundant vertices. Finally, we will show the robustness of our methodviadifferent experimental results. Moreover, we will study the stability of our method according to noise.

scholarly journals EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 25-31 ◽  
Author(s):  
J. Wang ◽  
R. Lindenbergh ◽  
M. Menenti
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Spatial Information ◽  
Computation Time ◽  
Point Clouds ◽  
The Other ◽  
Data Set

Laser scanning has become a well established surveying solution for obtaining 3D geo-spatial information on objects and environment. Nowadays scanners acquire up to millions of points per second which makes point cloud huge. Laser scanning is widely applied from airborne, carborne and stable platforms, resulting in point clouds obtained at different attitudes and with different extents. Working with such different large point clouds makes the determination of their overlapping area necessary but often time consuming. In this paper, a scalable point cloud intersection determination method is presented based on voxels. The method takes two overlapping point clouds as input. It consecutively resamples the input point clouds according to a preset voxel cell size. For all non-empty cells the center of gravity of the points in contains is computed. Consecutively for those centers it is checked if they are in a voxel cell of the other point cloud. The same process is repeated after interchanging the role of the two point clouds. The quality of the results is evaluated by the distance to the pints from the other data set. Also computation time and quality of the results are compared for different voxel cell sizes. The results are demonstrated on determining he intersection between an airborne and carborne laser point clouds and show that the proposed method takes 0.10%, 0.15%, 1.26% and 14.35% of computation time compared the the classic method when using cell sizes of of 10, 8, 5 and 3 meters respectively.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Sign in / Sign up

Export Citation Format

Share Document