scholarly journals AUTOMATIC EXTRACTION OF A NAVIGATION GRAPH INTENDED FOR INDOORGML FROM AN INDOOR POINT CLOUD

2019 ◽  
Vol IV-2/W5 ◽  
pp. 271-278
Author(s):  
P. Flikweert ◽  
R. Peters ◽  
L. Díaz-Vilariño ◽  
R. Voûte ◽  
B. Staats
Keyword(s):  
Laser Scanning ◽  
Medial Axis ◽  
Laser Scanner ◽  
Point Clouds ◽  
Essential Elements ◽  
Indoor Navigation ◽  
Indoor Environments ◽  
Architectural Drawings ◽  
The Public ◽  
First Results

<p><strong>Abstract.</strong> Indoor environments tend to be more complex and more populated when buildings are accessible to the public. The need for knowing where people are, how they can get somewhere or how to reach them in these buildings is thus equally increasing. In this research point clouds are used, obtained by dynamic laser scanning of a building, since we cannot rely on architectural drawings for maps and paths, which can be outdated. The presented method focuses on the creation of an indoor navigation graph, based on IndoorGML structure, in a fast and automated way, while retaining the type of walkable surface. In this paper the focus has been on door detection, because doors are essential elements in an indoor environment, seeing that they connect spaces and are a logical step in a route. This paper describes a way to detect doors using 3D Medial Axis Transform (MAT) combined with the intelligence stored in the path of a mobile laser scanner, showing good first results. Additionally different spaces (e.g. rooms and corridors) in the building are identified and slopes and stairs in walkable spaces are detected. This results in a navigation graph which can be stored in an IndoorGML structure.</p>

scholarly journals LINE SEGMENTATION OF 2D LASER SCANNER POINT CLOUDS FOR INDOOR SLAM BASED ON A RANGE OF RESIDUALS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 363-369 ◽  
Author(s):  
M. Peter ◽  
S. R. U. N. Jafri ◽  
G. Vosselman
Keyword(s):  
Laser Scanning ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Laser Scanner ◽  
Point Clouds ◽  
Indoor Environments ◽  
Six Degrees Of Freedom ◽  
Localization And Mapping ◽  
Feature Based ◽  
Definition Of

Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred method to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing three 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom simultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality and reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and consequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow objects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as separate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not handle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range of thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of <i>n</i> points with respect to the line is <i>σ</i>&amp;thinsp;/&amp;thinsp;&amp;radic;<i>n</i>. Our method, as shown by the experiments and the comparison to other methods, is able to deliver more accurate results than the two approaches it was tested against.

scholarly journals INDOOR 3D MODELING AND FLEXIBLE SPACE SUBDIVISION FROM POINT CLOUDS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 285-292 ◽  
Author(s):  
S. Nikoohemat ◽  
A. Diakité ◽  
S. Zlatanova ◽  
G. Vosselman
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Cell Decomposition ◽  
Indoor Navigation ◽  
Spatial Accessibility ◽  
Vector Model ◽  
Indoor Environments ◽  
Space Partitioning ◽  
Space Subdivision

<p><strong>Abstract.</strong> Indoor navigation can be a tedious process in a complex and unknown environment. It gets more critical when the first responders try to intervene in a big building after a disaster has occurred. For such cases, an accurate map of the building is among the best supports possible. Unfortunately, such a map is not always available, or generally outdated and imprecise, leading to error prone decisions. Thanks to advances in the laser scanning, accurate 3D maps can be built in relatively small amount of time using all sort of laser scanners (stationary, mobile, drone), although the information they provide is generally an unstructured point cloud. While most of the existing approaches try to extensively process the point cloud in order to produce an accurate architectural model of the scanned building, similar to a Building Information Model (BIM), we have adopted a space-focused approach. This paper presents our framework that starts from point-clouds of complex indoor environments, performs advanced processes to identify the 3D structures critical to navigation and path planning, and provides fine-grained navigation networks that account for obstacles and spatial accessibility of the navigating agents. The method involves generating a volumetric-wall vector model from the point cloud, identifying the obstacles and extracting the navigable 3D spaces. Our work contributes a new approach for space subdivision without the need of using laser scanner positions or viewpoints. Unlike 2D cell decomposition or a binary space partitioning, this work introduces a space enclosure method to deal with 3D space extraction and non-Manhattan World architecture. The results show more than 90% of spaces are correctly extracted. The approach is tested on several real buildings and relies on the latest advances in indoor navigation.</p>

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals INTEGRATING TOPOGRAPHIC, PHOTOGRAMMETRIC AND LASER SCANNING TECHNIQUES FOR A SCAN-TO-BIM PROCESS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 883-890
Author(s):  
M. Lo Brutto ◽  
E. Iuculano ◽  
P. Lo Giudice
Keyword(s):  
Laser Scanning ◽  
Building Information Modeling ◽  
Laser Scanner ◽  
Point Clouds ◽  
Parametric Modeling ◽  
Information Modeling ◽  
Survey Method ◽  
The Real ◽  
Building Information ◽  
Heritage Building

Abstract. The preservation of historic buildings can often be particularly difficult due to the lack of detailed information about architectural features, construction details, etc.. However, in recent years considerable technological innovation in the field of Architecture, Engineering, and Construction (AEC) has been achieved by the Building Information Modeling (BIM) process. BIM was developed as a methodology used mainly for new construction but, given its considerable potential, this approach can also be successfully used for existing buildings, especially for buildings of historical and architectural value. In this case, it is more properly referred to as Historic – or Heritage – Building Information Modeling (HBIM). In the HBIM process, it is essential to precede the parametric modeling phase of the building with a detailed 3D survey that allows the acquisition of all geometric information. This methodology, called Scan-to-BIM, involves the use of 3D survey techniques for the production of point clouds as a geometric “database” for parametric modeling. The Scan-to-BIM approach can have several issues relating to the complexity of the survey. The work aims to apply the Scan-to-BIM approach to the survey and modeling of a historical and architectural valuable building to test a survey method, based on integrating different techniques (topography, photogrammetry and laser scanning), that improves the data acquisition phase. The “Real Cantina Borbonica” (Cellar of Royal House of Bourbon) in Partinico (Sicily, Italy) was chosen as a case study. The work has allowed achieving the HBIM of the “Real Cantina Borbonica” and testing an approach based exclusively on a topographic constraint to merge in the same reference system all the survey data (laser scanner and photogrammetric point clouds).

scholarly journals COMBINATION OF THERMAL AND GEOMETRIC INFORMATION FOR BIM ENRICHMENT

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 719-725 ◽  
Author(s):  
H. Macher ◽  
M. Boudhaim ◽  
P. Grussenmeyer ◽  
M. Siroux ◽  
T. Landes
Keyword(s):  
Laser Scanning ◽  
Thermal Analyses ◽  
Laser Scanner ◽  
Point Clouds ◽  
Existing Buildings ◽  
Energy Audit ◽  
Geometric Information ◽  
Information Models ◽  
Building Information ◽  
Reduction Of Energy Consumption

<p><strong>Abstract.</strong> In the context of building renovation, infrared (IR) cameras are widely used to perform the energy audit of buildings. They allow analysing precisely the energetic performances of existing buildings and thermal analyses represent a key step for the reduction of energy consumption. They are also used to assess the thermal comfort of people living or working in a building. Building Information Models (BIM) are widespread to plan the rehabilitation of existing buildings and laser scanning is now commonly used to capture the geometry of buildings for as-built BIM creation. The combination of thermographic and geometric data presents a high number and variety of applications (Lagüela and Díaz-Vilariño, 2016). However, geometric and thermal information are generally acquired separately by different building stakeholders and thermal analyses are performed with independence of geometry. In this paper, the combination of thermal and geometric information is investigated for indoor of buildings. The aim of the project is to create 3D thermographic point clouds based on data acquired by a laser scanner and a thermal camera. Based on these point clouds, BIM models might be enriched with thermal information through the scan-to-BIM process.</p>

A novel method for extracting skeleton of fruit tree from 3D point clouds

2020 ◽  
pp. 2050051
Author(s):  
Shenglian lu ◽  
Guo Li ◽  
Jian Wang
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Tree Canopy ◽  
3D Point Clouds ◽  
Novel Method ◽  
Manual Interaction ◽  
Traditional Approaches

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

scholarly journals Automatic Indoor Reconstruction from Point Clouds in Multi-room Environments with Curved Walls

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3798 ◽  
Author(s):  
Fan Yang ◽  
Gang Zhou ◽  
Fei Su ◽  
Xinkai Zuo ◽  
Lei Tang ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Rapid Development ◽  
Point Clouds ◽  
Indoor Environments ◽  
Reconstruction Methods ◽  
Straight Line ◽  
Recent Developments ◽  
A Cell ◽  
Curved Walls ◽  
Indoor Reconstruction

Recent developments in laser scanning systems have inspired substantial interest in indoor modeling. Semantically rich indoor models are required in many fields. Despite the rapid development of 3D indoor reconstruction methods for building interiors from point clouds, the indoor reconstruction of multi-room environments with curved walls is still not resolved. This study proposed a novel straight and curved line tracking method followed by a straight line test. Robust parameters are used, and a novel straight line regularization method is achieved using constrained least squares. The method constructs a cell complex with both straight lines and curved lines, and the indoor reconstruction is transformed into a labeling problem that is solved based on a novel Markov Random Field formulation. The optimal labeling is found by minimizing an energy function by applying a minimum graph cut approach. Detailed experiments were conducted, and the results indicate that the proposed method is well suited for 3D indoor modeling in multi-room indoor environments with curved walls.

Influence of ULS data acquisition characteristics on the achievable stem reconstruction accuracies

2020 ◽  
Author(s):  
Moritz Bruggisser ◽  
Johannes Otepka ◽  
Norbert Pfeifer ◽  
Markus Hollaus
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Diameter ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Detection Rates ◽  
Point Count ◽  
Tree Stem

&lt;p&gt;Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation.&amp;#160;&lt;br&gt;The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.&lt;/p&gt;

scholarly journals INDOOR NAVIGATION FROM POINT CLOUDS: 3D MODELLING AND OBSTACLE DETECTION

2016 ◽  
Vol XLI-B4 ◽  
pp. 275-281 ◽  
Author(s):  
L. Díaz-Vilariño ◽  
P. Boguslawski ◽  
K. Khoshelham ◽  
H. Lorenzo ◽  
L. Mahdjoubi
Keyword(s):  
Laser Scanner ◽  
Route Planning ◽  
Point Clouds ◽  
3D Modelling ◽  
Obstacle Detection ◽  
Indoor Navigation ◽  
3D Point Clouds ◽  
Emergency Assistance ◽  
Planning Methodology ◽  
Real State

In the recent years, indoor modelling and navigation has become a research of interest because many stakeholders require navigation assistance in various application scenarios. The navigational assistance for blind or wheelchair people, building crisis management such as fire protection, augmented reality for gaming, tourism or training emergency assistance units are just some of the direct applications of indoor modelling and navigation. &lt;br&gt;&lt;br&gt; Navigational information is traditionally extracted from 2D drawings or layouts. Real state of indoors, including opening position and geometry for both windows and doors, and the presence of obstacles is commonly ignored. &lt;br&gt;&lt;br&gt; In this work, a real indoor-path planning methodology based on 3D point clouds is developed. The value and originality of the approach consist on considering point clouds not only for reconstructing semantically-rich 3D indoor models, but also for detecting potential obstacles in the route planning and using these for readapting the routes according to the real state of the indoor depictured by the laser scanner.

scholarly journals Geoarchaeological site documentation and analysis of 3D data derived by terrestrial laser scanning

2014 ◽  
Vol II-5 ◽  
pp. 173-179 ◽  
Author(s):  
D. Hoffmeister ◽  
S. Zellmann ◽  
K. Kindermann ◽  
A. Pastoors ◽  
U. Lang ◽  
...  
Keyword(s):  
Cross Sections ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
3D Data ◽  
Research Problems ◽  
Survey Results ◽  
Rtk Gps ◽  
Derived Data

Terrestrial laser scanning was conducted to document and analyse sites of geoarchaeological interest in Jordan, Egypt and Spain. In those cases, the terrestrial laser scanner LMS-Z420i from Riegl was used in combination with an accurate RTK-GPS for georeferencing of the point clouds. Additionally, local surveying networks were integrated by established transformations and used for indirect registration purposes. All data were integrated in a workflow that involves different software and according results. The derived data were used for the documentation of the sites by accurate plans and cross-sections. Furthermore, the 3D data were analysed for geoarchaeological research problems, such as volumetric determinations, the ceiling thickness of a cave and lighting simulations based on path tracing. The method was reliable in harsh environmental conditions, but the weight of the instrument, the measuring time and the minimum measurement distance were a drawback. However, generally an accurate documentation of the sites was possible. Overall, the integration in a 3D GIS is easily possible by the accurate georeference of the derived data. In addition, local survey results are also implemented by the established transformations. Enhanced analyses based on the derived 3D data shows promising results.

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