Automatic non-rigid registration of multi-strip point clouds from mobile laser scanning systems

2017 ◽  
Vol 39 (6) ◽  
pp. 1713-1728 ◽  
Author(s):  
Li Yan ◽  
Junxiang Tan ◽  
Hua Liu ◽  
Hong Xie ◽  
Changjun Chen
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Rigid Registration ◽  
Scanning Systems

scholarly journals Segmentation of LiDAR Data Using Multilevel Cube Code

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
So-Young Park ◽  
Dae Geon Lee ◽  
Eun Jin Yoo ◽  
Dong-Cheon Lee
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Lidar Data ◽  
Chain Code ◽  
Cloud Data ◽  
Airborne Laser ◽  
Wide Range ◽  
Scanning Systems

Light detection and ranging (LiDAR) data collected from airborne laser scanning systems are one of the major sources of spatial data. Airborne laser scanning systems have the capacity for rapid and direct acquisition of accurate 3D coordinates. Use of LiDAR data is increasing in various applications, such as topographic mapping, building and city modeling, biomass measurement, and disaster management. Segmentation is a crucial process in the extraction of meaningful information for applications such as 3D object modeling and surface reconstruction. Most LiDAR processing schemes are based on digital image processing and computer vision algorithms. This paper introduces a shape descriptor method for segmenting LiDAR point clouds using a “multilevel cube code” that is an extension of the 2D chain code to 3D space. The cube operator segments point clouds into roof surface patches, including superstructures, removes unnecessary objects, detects the boundaries of buildings, and determines model key points for building modeling. Both real and simulated LiDAR data were used to verify the proposed approach. The experiments demonstrated the feasibility of the method for segmenting LiDAR data from buildings with a wide range of roof types. The method was found to segment point cloud data effectively.

scholarly journals Fusion of range-based data and image-based datasets for efficient documentation of cultural heritage objects and sites

2015 ◽  
Vol XL-5/W7 ◽  
pp. 277-281 ◽  
Author(s):  
J. L. Lerma ◽  
M. Cabrelles ◽  
S. Navarro
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Indirect Methods ◽  
Recording Project ◽  
Dense Point ◽  
The Times ◽  
Cultural Heritage Objects ◽  
Scanning Systems ◽  
Direct And Indirect Methods

Nowadays it is possible to measure accurately dense point clouds either with aerial/terrestrial laser scanning systems or with imagebased solutions (namely based on photogrammetric computer vision algorithms such as structure-from-motion (SfM)), from which highly detailed 3D models can be achieved. Besides, direct tools in the form of simple devices such as rulers, compass and plumblines are usually required in simple metric surveys, as well as high-end surveying and geodetic instruments such as robotized imagebased total stations and GNSS (probably to a lesser degree but still required) to set the archaeological/architectural recording project in a global reference frame. With all this gamut of image-based and range-based sensors and datasets (in the form of coordinates, point clouds or 3D models), in different coordinate systems (most of the times local for each device), lack of uniform scale, orientation and levelling, the fusion of data tends to be cumbersome. This paper presents an efficient way to fuse and merge different datasets in the form of point clouds/3D models and geodetic/UTM coordinates. The new developed 3DVEM – Register GEO software is able to handle datasets coming from both direct and indirect methods in order to provide unified and precise deliverables.

scholarly journals A MARKER-FREE CALIBRATION METHOD FOR MOBILE LASER SCANNING POINT CLOUDS CORRECTION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 347-354
Author(s):  
B. Yang ◽  
Y. Li ◽  
X. Zou ◽  
Z. Dong
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Calibration Method ◽  
Global Alignment ◽  
High Definition ◽  
Marker Free ◽  
Adjacent Segments ◽  
Scanning Systems ◽  
Coarse To Fine

Abstract. Mobile laser scanning systems (MLS) have been widely used in collecting three-dimensional point clouds for many applications, such as 3D mapping, road facilities inventory and high definition map. Although MLS is calibrated accurately to obtain precise locations of point clouds, it is still challenging to obtain precise locations of point clouds in the areas of GPS signal denied or narrow streets with high dense buildings, resulting in uneven position deviations of point clouds between the overlapping trajectory areas. In this paper, a marker-free calibration method is proposed to solve the above problems. The proposed method firstly partitions the trajectory into segments according to the error distribution while collecting the point clouds. Secondly, the features in each overlapped area are extracted and a kind of Locally Aggregated Descriptors are built for the matching. Thirdly, a coarse-to-fine pairwise point clouds alignment is applied on the overlapping areas. Finally, the global alignment of point clouds is fulfilled with minimizing the position deviations between the overlapping areas and the adjacent segments. The proposed method has been used to correct the point clouds from several different MLSs. Experiments show that this method automatically locates and corrects the uneven position deviations in terms of good robustness and efficiencies. Besides, it proves that the proposed method is also an easy-to-use tool for the automatic correction of MLS point clouds position and boresights.

scholarly journals Design and Evaluation of a Permanently Installed Plane-Based Calibration Field for Mobile Laser Scanning Systems

2020 ◽  
Vol 12 (3) ◽  
pp. 555 ◽  
Author(s):  
Erik Heinz ◽  
Christoph Holst ◽  
Heiner Kuhlmann ◽  
Lasse Klingbeil
Keyword(s):  
Reference Point ◽  
Laser Scanning ◽  
Point Clouds ◽  
Measurement Unit ◽  
Scanning System ◽  
Control Points ◽  
Lever Arm ◽  
Laser Scanning System ◽  
The Impact ◽  
Scanning Systems

Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of <1 ... 1.5 mm for the lever arm and <0.005 ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

scholarly journals AUTOMATIC CLASSIFICATION OF POINT CLOUDS FOR HIGHWAY DOCUMENTATION

2018 ◽  
Vol 58 (3) ◽  
pp. 165 ◽  
Author(s):  
Martina Hůlková ◽  
Karel Pavelka ◽  
Eva Matoušková
Keyword(s):  
Image Processing ◽  
Laser Scanning ◽  
Point Clouds ◽  
Image Processing Techniques ◽  
Raster Image ◽  
Specific Analysis ◽  
Processing Techniques ◽  
Scanning Systems ◽  
Crucial Part

Mobile laser scanning systems confirmed the capability for detailed roadway documentation. Hand in hand with enormous datasets acquired by these systems is the increase in the demands on the fast and effective processing of these datasets. The crucial part of the roadway datasets processing, as well as in many other applications, is the extraction of objects of interest from point clouds. In this work, an approach to the rough classification of mobile laser scanning data based on raster image processing techniques is presented. The developed method offers a solution for a computationally low demanding classification of the highway environment. The aim of this method is to provide a background for the easier use of more sophisticated algorithms and a specific analysis. The method is evaluated using different metrics on a 1.8km long dataset obtained by LYNX Mobile Mapper over a highway.

scholarly journals QUALITY ANALYSIS AND CORRECTION OF MOBILE BACKPACK LASER SCANNING DATA

2016 ◽  
Vol III-1 ◽  
pp. 41-47 ◽  
Author(s):  
P. Rönnholm ◽  
X. Liang ◽  
A. Kukko ◽  
A. Jaakkola ◽  
J. Hyyppä
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Quality Analysis ◽  
Scanning System ◽  
Forest Environment ◽  
Analysis Process ◽  
Ground Point ◽  
Aerial Vehicle ◽  
Laser Scanning System ◽  
Scanning Systems

Backpack laser scanning systems have emerged recently enabling fast data collection and flexibility to make measurements also in areas that cannot be reached with, for example, vehicle-based laser scanners. Backpack laser scanning systems have been developed both for indoor and outdoor use. We have developed a quality analysis process in which the quality of backpack laser scanning data is evaluated in the forest environment. The reference data was collected with an unmanned aerial vehicle (UAV) laser scanning system. The workflow included noise filtering, division of data into smaller patches, ground point extraction, ground data decimation, and ICP registration. As a result, we managed to observe the misalignments of backpack laser scanning data for 97 patches each including data from circa 10 seconds period of time. This evaluation revealed initial average misalignments of 0.227 m, 0.073 and -0.083 in the easting, northing and elevation directions, respectively. Furthermore, backpack data was corrected according to the ICP registration results. Our correction algorithm utilized the time-based linear transformation of backpack laser scanning point clouds. After the correction of data, the ICP registration was run again. This revealed remaining misalignments between the corrected backpack laser scanning data and the original UAV data. We found average misalignments of 0.084, 0.020 and -0.005 meters in the easting, northing and elevation directions, respectively.

scholarly journals QUALITY ANALYSIS AND CORRECTION OF MOBILE BACKPACK LASER SCANNING DATA

2016 ◽  
Vol III-1 ◽  
pp. 41-47 ◽  
Author(s):  
P. Rönnholm ◽  
X. Liang ◽  
A. Kukko ◽  
A. Jaakkola ◽  
J. Hyyppä
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Quality Analysis ◽  
Scanning System ◽  
Forest Environment ◽  
Analysis Process ◽  
Ground Point ◽  
Aerial Vehicle ◽  
Laser Scanning System ◽  
Scanning Systems

Backpack laser scanning systems have emerged recently enabling fast data collection and flexibility to make measurements also in areas that cannot be reached with, for example, vehicle-based laser scanners. Backpack laser scanning systems have been developed both for indoor and outdoor use. We have developed a quality analysis process in which the quality of backpack laser scanning data is evaluated in the forest environment. The reference data was collected with an unmanned aerial vehicle (UAV) laser scanning system. The workflow included noise filtering, division of data into smaller patches, ground point extraction, ground data decimation, and ICP registration. As a result, we managed to observe the misalignments of backpack laser scanning data for 97 patches each including data from circa 10 seconds period of time. This evaluation revealed initial average misalignments of 0.227 m, 0.073 and -0.083 in the easting, northing and elevation directions, respectively. Furthermore, backpack data was corrected according to the ICP registration results. Our correction algorithm utilized the time-based linear transformation of backpack laser scanning point clouds. After the correction of data, the ICP registration was run again. This revealed remaining misalignments between the corrected backpack laser scanning data and the original UAV data. We found average misalignments of 0.084, 0.020 and -0.005 meters in the easting, northing and elevation directions, respectively.

scholarly journals A Hierarchical Clustering Method to Repair Gaps in Point Clouds of Powerline Corridor for Powerline Extraction

2021 ◽  
Vol 13 (8) ◽  
pp. 1502
Author(s):  
Yongzhao Fan ◽  
Rong Zou ◽  
Xiaoyun Fan ◽  
Rendong Dong ◽  
Mengyou Xie
Keyword(s):  
Hierarchical Clustering ◽  
Laser Scanning ◽  
Point Clouds ◽  
Test Results ◽  
Clustering Method ◽  
Point Density ◽  
Slope Change ◽  
Neighborhood Relations ◽  
Scanning Systems ◽  
Night Period

Powerline detection is becoming a significant issue for powerline monitoring and maintenance, which further ensures transmission security. As an efficient method, laser scanning has attracted considerable attention in powerline detection for its high precision and robustness during the night period. However, due to occlusion and varying point density, gaps will appear in scans and greatly influence powerline detection by over–clustering, insufficient extraction, or misclassification in existing methods. Moreover, this situation will be worse in terrestrial laser scanning (TLS), because TLS suffers more from gaps due to its unique ground–based scanning mode compared to other laser scanning systems. Thereby, this paper explores a robust method to repair gaps for extracting powerlines from TLS data. Firstly, a hierarchical clustering method is used to extract the powerlines. During the clustering, gaps are repaired based on neighborhood relations of powerline candidates, and repaired gaps can create continuous neighborhood relations that ensure the execution of the clustering method in return. Test results show that the hierarchical clustering method is robust in powerline extraction with repaired gaps. Secondly, reconstruction is performed for further detection. Pylon–powerline connections are found by the slope change method, and powerlines with multi–span are successfully fitted using these connections. Experiment shows that it is feasible to find connections for multi–span reconstruction.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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