scholarly journals Lidar-equipped uav for building information modelling

2014 ◽  
Vol XL-5 ◽  
pp. 523-527 ◽  
Author(s):  
D. Roca ◽  
J. Armesto ◽  
S. Lagüela ◽  
L. Díaz-Vilariño
Keyword(s):  
Point Cloud ◽  
Electronic Devices ◽  
3D Models ◽  
Sensor Technology ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Cloud Data ◽  
Building Information ◽  
Innovative Solution ◽  
Capture Point

The trend to minimize electronic devices in the last decades accounts for Unmanned Airborne Vehicles (UAVs) as well as for sensor technologies and imaging devices, resulting in a strong revolution in the surveying and mapping industries. However, only within the last few years the LIDAR sensor technology has achieved sufficiently reduction in terms of size and weight to be considered for UAV platforms. This paper presents an innovative solution to capture point cloud data from a Lidar-equipped UAV and further perform the 3D modelling of the whole envelope of buildings in BIM format. A mini-UAV platform is used (weigh less than 5 kg and up to 1.5 kg of sensor payload), and data from two different acquisition methodologies is processed and compared with the aim at finding the optimal configuration for the generation of 3D models of buildings for energy studies

scholarly journals From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

2020 ◽  
Vol 12 (7) ◽  
pp. 1094 ◽  
Author(s):  
Mesrop Andriasyan ◽  
Juan Moyano ◽  
Juan Enrique Nieto-Julián ◽  
Daniel Antón
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Accuracy Assessment ◽  
Parametric Modelling ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Historical Interpretation ◽  
Information Modelling ◽  
Cloud Data ◽  
Building Information

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

scholarly journals A Framework for Automated Acquisition and Processing of As-Built Data with Autonomous Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4513 ◽  
Author(s):  
Henk Freimuth ◽  
Markus König
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Planning And Scheduling ◽  
Robot Operating System ◽  
Cloud Data ◽  
Deterministic Routing ◽  
Stereo Cameras ◽  
Building Information ◽  
Aerial Vehicle

Planning and scheduling in construction heavily depend on current information about the state of construction processes. However, the acquisition process for visual data requires human personnel to take photographs of construction objects. We propose using unmanned aerial vehicle (UAVs) for automated creation of images and point cloud data of particular construction objects. The method extracts locations of objects that require inspection from Four Dimensional Building Information Modelling (4D-BIM). With this information at hand viable flight missions around the known structures of the construction site are computed. During flight, the UAV uses stereo cameras to detect and avoid any obstacles that are not known to the model, for example moving humans or machinery. The combination of pre-computed waypoint missions and reactive avoidance ensures deterministic routing from takeoff to landing and operational safety for humans and machines. During flight, an additional software component compares the captured point cloud data with the model data, enabling automatic per-object completion checking or reconstruction. The prototype is developed in the Robot Operating System (ROS) and evaluated in Software-In-The-Loop (SITL) simulations for the sake of being executable on real UAVs.

scholarly journals BUILDING INFORMATION MODELLING OF A MULTI STOREY BUILDING USING TERRESTRIAL LASER SCANNER AND VISUALISATION USING POTREE: AN OPEN SOURCE POINT CLOUD RENDERER

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 421-426
Author(s):  
A. Yeshwanth Kumar ◽  
M. A. Noufia ◽  
K. A. Shahira ◽  
A. M. Ramiya
Keyword(s):  
Open Source ◽  
Web Application ◽  
Point Cloud ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Cloud Data ◽  
Building Information ◽  
Storey Building

Abstract. With the rapid development in infrastructure, the need to document man-made structures is in increasing demand and inevitable. Such a process of digital documentation of buildings is called Building Information Modelling (BIM). Conventional techniques of BIM involve manual drafting & modelling using computer aided design, drafting & modelling software. Although these techniques are more accurate, given the increase in the size and complexity of modern structures, it would be tedious and time consuming for such manual work. It is in this context LiDAR shows great potential to simplify this task. Laser scanning enables rapid mapping of a building with a high degree of spatial accuracy. Since the spatial point sampling distance of any LiDAR scanner is usually in the order of centimetres or millimetres, this has potential not only to generate high density scans of the building but also to identify even the smallest defects in a structure. This facilitates using LiDAR to study the serviceability of a building. In this project, the feasibility of using a terrestrial laser scanner (TLS) to scan a multi-storey building was investigated. Additionally, the reliability of Potree for visualising point cloud data was tested. Potree is an open-source WebGL based point cloud renderer. Potree enables us to render point clouds and visualise in a portable web application. This application is also capable of making measurements of high accuracy on the 3D model of the library. This could serve to be of great utility in surveying applications. The object of study was chosen as a six-storey building, each floor having differing layouts. Two of these storeys were below ground surface level which also proved to be a test for the reliability of TLS in challenging terrain. The building has a towering height and large footprint which made it a perfect candidate for this project. A total of 54 scans (44 interior scans and 10 exterior scans of the library) were acquired with each subsequent scan station not more than 10m apart from the previous one. This data was brought to the lab for further processing. The processing was carried out using open-source software packages (LAStools, CloudCompare, etc). After processing, the complete point cloud data had 483,292,994 points. In order to make the data easier to handle, spatial sub-sampling of the data was done after which the final point cloud had 87,789,548 points. Finally, this sub-sampled point cloud was published using the open source Potree Converter into an interactive web application.

scholarly journals Topology Reconstruction of BIM Wall Objects from Point Cloud Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1800 ◽  
Author(s):  
Maarten Bassier ◽  
Maarten Vergauwen
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Evaluation Framework ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Current State ◽  
Building Information ◽  
Popular Subject ◽  
Best Fit

The processing of remote sensing measurements to Building Information Modeling (BIM) is a popular subject in current literature. An important step in the process is the enrichment of the geometry with the topology of the wall observations to create a logical model. However, this remains an unsolved task as methods struggle to deal with the noise, incompleteness and the complexity of point cloud data of building scenes. Current methods impose severe abstractions such as Manhattan-world assumptions and single-story procedures to overcome these obstacles, but as a result, a general data processing approach is still missing. In this paper, we propose a method that solves these shortcomings and creates a logical BIM model in an unsupervised manner. More specifically, we propose a connection evaluation framework that takes as input a set of preprocessed point clouds of a building’s wall observations and compute the best fit topology between them. We transcend the current state of the art by processing point clouds of both straight, curved and polyline-based walls. Also, we consider multiple connection types in a novel reasoning framework that decides which operations are best fit to reconstruct the topology of the walls. The geometry and topology produced by our method is directly usable by BIM processes as it is structured conform the IFC data structure. The experimental results conducted on the Stanford 2D-3D-Semantics dataset (2D-3D-S) show that the proposed method is a promising framework to reconstruct complex multi-story wall elements in an unsupervised manner.

scholarly journals IFCWALL RECONSTRUCTION FROM UNSTRUCTURED POINT CLOUDS

2018 ◽  
Vol IV-2 ◽  
pp. 33-39 ◽  
Author(s):  
M. Bassier ◽  
R. Klein ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Input Data ◽  
Initial Point ◽  
Point Clouds ◽  
Information Modeling ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Building Information ◽  
Wall Geometry

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data.<br>In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

scholarly journals BIM RECONSTRUCTION: AUTOMATED PROCEDURAL MODELING FROM POINT CLOUD DATA

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 53-60
Author(s):  
M. Bassier ◽  
L. Mattheuwsen ◽  
M. Vergauwen
Keyword(s):  
Point Cloud ◽  
Reconstruction Algorithm ◽  
Information Modeling ◽  
Data Sets ◽  
Point Cloud Data ◽  
Ongoing Research ◽  
Cloud Data ◽  
Structure Information ◽  
Wide Range ◽  
Building Information

Abstract. The reconstruction of Building Information Modeling objects for as-built modeling is currently the subject of ongoing research. A popular method is to extract structure information from point cloud data to create a set of parametric objects. This requires the interpretation of the point cloud data which currently is a manual and labor intensive procedure. Automated processes have to cope with excessive occlusions and clutter in the data sets. To create an as-built BIM, it is vital to reconstruct the building’s structure i.e. wall geometry prior to the reconstruction of other objects.In this work, a novel method is presented to automatically reconstruct as-built BIM for generic buildings. We presented an unsupervised method that procedurally models the geometry of the walls based on point cloud data. A bottom-up process is defined where consecutively higher level information is extracted from the point cloud data using pre-trained machine learning models. Prior to the reconstruction, the data is segmented, classified and clustered to retrieve all the available observations of the walls. The resulting geometry is processed by the reconstruction algorithm. First, the necessary information is extracted from the observations for the creation of parametric solid objects. Subsequently, the final walls are created by updating their topology. The method is tested on a variety of scenes and shows promising results to reliably and accurately create as-built models. The accuracy of the generated geometry is similar to the precision of expert modelers. A key advantage is that that the algorithm creates Revit and Rhino native objects which makes the geometry directly applicable to a wide range of applications.

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