scholarly journals An Effectively Dynamic Path Optimization Approach for the Tree Skeleton Extraction from Portable Laser Scanning Point Clouds

2021 ◽  
Vol 14 (1) ◽  
pp. 94
Author(s):  
Sheng Xu ◽  
Xin Li ◽  
Jiayan Yun ◽  
Shanshan Xu
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Structure Study ◽  
Path Optimization ◽  
Scanning System ◽  
Input Tree ◽  
Optimization Approach ◽  
Skeleton Extraction ◽  
Branching Points ◽  
Dynamic Path

One key step to the tree structure study is skeleton processing. Although there are lots of extraction approaches, the existing methods have paid less attention to extraction effectiveness, which highly use redundant points to formulate the skeleton and bring difficulties to the subsequent 3D modeling. This work proposes a four-step framework for the purpose of skeleton extraction. Firstly, candidate skeleton points are filtered from input data based on the spatial slice projection and grouped using the Euclidean distance analysis. Secondly, a key dynamic path optimization step is used to formulate a tree skeleton using the candidate point information. Thirdly, the optimized path is filled by interpolating points to achieve complete skeletons. Finally, short skeletons are removed based on the distance between branching points and ending points, and then, the extraction skeletons are smoothed for improving the visual quality. Our main contribution lies in that we find the global minimization cost path from every point to the root using a novel energy function. The formulated objective function contains a data term to constrain the distance between points and paths, and a smoothness term to constrain the direction continuities. Experimental scenes include three different types of trees, and input point clouds are collected by a portable laser scanning system. Skeleton extraction results demonstrate that we achieved completeness and correctness of 81.10% and 99.21%. respectively. Besides, our effectiveness is up to 79.26%, which uses only 5.82% of the input tree points in the skeleton representation, showing a promising effective solution for the tree skeleton and structure study.

scholarly journals PROGRESS ON ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1709-1713
Author(s):  
C. Wang ◽  
Y. Dai ◽  
N. El-Sheimy ◽  
C. Wen ◽  
G. Retscher ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Low Cost ◽  
Point Clouds ◽  
Indoor Positioning ◽  
Benchmark Dataset ◽  
Scanning System ◽  
Indoor Environments ◽  
Building Management ◽  
Common Framework

<p><strong>Abstract.</strong> This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on the elements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building management and navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone indoor positioning methods.</p>

scholarly journals Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

2018 ◽  
Vol 10 (9) ◽  
pp. 1403 ◽  
Author(s):  
Jianwei Wu ◽  
Wei Yao ◽  
Przemyslaw Polewski
Keyword(s):  
Tree Species ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Scanning System ◽  
Urban Road ◽  
Road Corridors ◽  
Laser Scanning System

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only.

scholarly journals ANALYSIS OF THE POSSIBILITIES OF USING LOW-COST SCANNING SYSTEM IN 3D MODELING

2016 ◽  
Vol XLI-B3 ◽  
pp. 261-267
Author(s):  
M. Kedzierski ◽  
D. Wierzbickia ◽  
A. Fryskowska ◽  
B. Chlebowska
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Laser Scanning ◽  
Low Cost ◽  
Comparative Method ◽  
Research Work ◽  
Laser Scanner ◽  
Method Comparison ◽  
Point Clouds ◽  
Scanning System

The laser scanning technique is still a very popular and fast growing method of obtaining information on modeling 3D objects. The use of low-cost miniature scanners creates new opportunities for small objects of 3D modeling based on point clouds acquired from the scan. The same, the development of accuracy and methods of automatic processing of this data type is noticeable. The article presents methods of collecting raw datasets in the form of a point-cloud using a low-cost ground-based laser scanner FabScan. As part of the research work 3D scanner from an open source FabLab project was constructed. In addition, the results for the analysis of the geometry of the point clouds obtained by using a low-cost laser scanner were presented. Also, some analysis of collecting data of different structures (made of various materials such as: glass, wood, paper, gum, plastic, plaster, ceramics, stoneware clay etc. and of different shapes: oval and similar to oval and prism shaped) have been done. The article presents two methods used for analysis: the first one - visual (general comparison between the 3D model and the real object) and the second one - comparative method (comparison between measurements on models and scanned objects using the mean error of a single sample of observations). The analysis showed, that the low-budget ground-based laser scanner FabScan has difficulties with collecting data of non-oval objects. Items built of glass painted black also caused problems for the scanner. In addition, the more details scanned object contains, the lower the accuracy of the collected point-cloud is. Nevertheless, the accuracy of collected data (using oval-straight shaped objects) is satisfactory. The accuracy, in this case, fluctuates between ± 0,4 mm and ± 1,0 mm whereas when using more detailed objects or a rectangular shaped prism the accuracy is much more lower, between 2,9 mm and ± 9,0 mm. Finally, the publication presents the possibility (for the future expansion of research) of modernization FabScan by the implementation of a larger amount of camera-laser units. This will enable spots the registration , that are less visible.

scholarly journals APPLICATIONS OF 3D-EDGE DETECTION FOR ALS POINT CLOUD

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 277-283 ◽  
Author(s):  
H. Ni ◽  
X. G. Lin ◽  
J. X. Zhang
Keyword(s):  
Edge Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Extraction Methods ◽  
Sensor Technology ◽  
Scanning System ◽  
Geometric Properties ◽  
Dense Point ◽  
Fast Development ◽  
3D Edge Detection

Edge detection has been one of the major issues in the field of remote sensing and photogrammetry. With the fast development of sensor technology of laser scanning system, dense point clouds have become increasingly common. Precious 3D-edges are able to be detected from these point clouds and a great deal of edge or feature line extraction methods have been proposed. Among these methods, an easy-to-use 3D-edge detection method, AGPN (Analyzing Geometric Properties of Neighborhoods), has been proposed. The AGPN method detects edges based on the analysis of geometric properties of a query point’s neighbourhood. The AGPN method detects two kinds of 3D-edges, including boundary elements and fold edges, and it has many applications. This paper presents three applications of AGPN, i.e., 3D line segment extraction, ground points filtering, and ground breakline extraction. Experiments show that the utilization of AGPN method gives a straightforward solution to these applications.

scholarly journals A FEASIBILITY STUDY ON USE OF GENERIC MOBILE LASER SCANNING SYSTEM FOR DETECTING ASPHALT PAVEMENT CRACKS

2016 ◽  
Vol XLI-B1 ◽  
pp. 545-549 ◽  
Author(s):  
Xinqu Chen ◽  
Jonathan Li
Keyword(s):  
Laser Scanning ◽  
Crack Detection ◽  
Matched Filter ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Noise Removal ◽  
Scanning System ◽  
Pass Filter ◽  
3D Point Clouds ◽  
High Pass

This study aims to automatically detect pavement cracks on urban roads by employing the 3D point clouds acquired by a mobile laser scanning (MLS) system. Our method consists of four steps: ground point filtering, high-pass convolution, matched filtering, and noise removal. First, a voxel-based upward growing method is applied to construct Digital Terrain Model (DTM) of the road surface. Then, a high-pass filter convolutes the DTM to detect local elevation changes that may embed cracking information. Next, a two-step matched filter is applied to extract crack features. Lastly, a noise removal process is conducted to refine the results. Instead of using MLS intensity, this study takes advantages of the MLS elevation information to perform automated crack detection from large-volume, mixed-density, unstructured MLS point clouds. Four types of cracks including longitudinal, transvers, random, and alligator cracks are detected. Our results demonstrated that the proposed method works well with the RIEGL VMX-450 point clouds and can detect cracks in moderate-to-severe severity (13 - 25 mm) within a 200 m by 30 m urban road segment located in Kingston, Ontario, at one time. Due to the resolution capability, small cracks with slight severity remain unclear in the MLS point cloud.

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 Systematic Comparison of Power Corridor Classification Methods from ALS Point Clouds

2019 ◽  
Vol 11 (17) ◽  
pp. 1961
Author(s):  
Shuwen Peng ◽  
Xiaohuan Xi ◽  
Cheng Wang ◽  
Pinliang Dong ◽  
Pu Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Laser Scanning ◽  
Point Clouds ◽  
Classification Performance ◽  
Gradient Boosting ◽  
Scanning System ◽  
Classification Methods ◽  
Class Distribution ◽  
Classification Feature

Power corridor classification using LiDAR (light detection and ranging) point clouds is an important means for power line inspection. Many supervised classification methods have been used for classifying power corridor scenes, such as using random forest (RF) and JointBoost. However, these studies did not systematically analyze all the relevant factors that affect the classification, including the class distribution, feature selection, classifier type and neighborhood radius for classification feature extraction. In this study, we examine these factors using point clouds collected by an airborne laser scanning system (ALS). Random forest shows strong robustness to various pylon types. When classifying complex scenes, the gradient boosting decision tree (GBDT) shows good generalization. Synthetically, considering performance and efficiency, RF is very suitable for power corridor classification. This study shows that balanced learning leads to poor classification performance in the current scene. Data resampling for the original unbalanced dataset may not be necessary. The sensitivity analysis shows that the optimal neighborhood radius for feature extraction of different objects may be different. Scale invariance and automatic scale selection methods should be further studied. Finally, it is suggested that RF, original unbalanced class distribution, and complete feature set should be considered for power corridor classification in most cases.

scholarly journals Absolute Positioning and Orientation of MLSS in a Subway Tunnel Based on Sparse Point-Assisted DR

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 645
Author(s):  
Qian Wang ◽  
Chao Tang ◽  
Cuijun Dong ◽  
Qingzhou Mao ◽  
Fei Tang ◽  
...  
Keyword(s):  
Coordinate System ◽  
Laser Scanning ◽  
Local Coordinate System ◽  
Point Clouds ◽  
Measurement Unit ◽  
Scanning System ◽  
Control Points ◽  
Starting Point ◽  
Inspection Work ◽  
Subway Tunnels

When performing the inspection of subway tunnels, there is an immense amount of data to be collected and the time available for inspection is short; however, the requirement for inspection accuracy is high. In this study, a mobile laser scanning system (MLSS) was used for the inspection of subway tunnels, and the key technology of the positioning and orientation system (POS) was investigated. We utilized the inertial measurement unit (IMU) and the odometer as the core sensors of the POS. The initial attitude of the MLSS was obtained by using a static initial alignment method. Considering that there is no global navigation satellite system (GNSS) signal in a subway, the forward and backward dead reckoning (DR) algorithm was used to calculate the positions and attitudes of the MLSS from any starting point in two directions. While the MLSS passed by the control points distributed on both sides of the track, the local coordinates of the control points were transmitted to the center of the MLSS by using the ranging information of the laser scanner. Then, a four-parameter transformation method was used to correct the error of the POS and transform the 3-D state information of the MLSS from a navigation coordinate system (NCS) to a local coordinate system (LCS). This method can completely eliminate a MLSS’s dependence on GNSS signals, and the obtained positioning and attitude information can be used for point cloud data fusion to directly obtain the coordinates in the LCS. In a tunnel of the Beijing–Zhangjiakou high-speed railway, when the distance interval of the control points used for correction was 120 m, the accuracy of the 3-D coordinates of the point clouds was 8 mm, and the experiment also showed that it takes less than 4 h to complete all the inspection work for a 5–6 km long tunnel. Further, the results from the inspection work of Wuhan subway lines showed that when the distance intervals of the control points used for correction were 60 m, 120 m, 240 m, and 480 m, the accuracies of the 3-D coordinates of the point clouds in the local coordinate system were 4 mm, 6 mm, 7 mm, and 8 mm, respectively.

scholarly journals ISPRS BENCHMARK ON MULTISENSORY INDOOR MAPPING AND POSITIONING

2020 ◽  
Vol V-5-2020 ◽  
pp. 117-123
Author(s):  
C. Wang ◽  
Y. Dai ◽  
N. Elsheimy ◽  
C. Wen ◽  
G. Retscher ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Point Clouds ◽  
Indoor Positioning ◽  
Benchmark Dataset ◽  
Scanning System ◽  
Indoor Environments ◽  
Process Data ◽  
Localization And Mapping ◽  
Common Framework

Abstract. In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project.

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