Extraction of main levels of a building from a large point cloud

Horizontal levels are references entities, the base of man-made environments. Their creation is the first step for various applications including the BIM (Building Information Modelling). BIM is an emerging methodology, widely used for new constructions, and increasingly applied to existing buildings (scan-to-BIM). The as-built BIM process is still mainly manual or semi-automatic and therefore is highly time-consuming. The automation of the as-built BIM is a challenging topic among the research community. This study is part of an ongoing research into the scan-to-BIM process regarding the extraction of the principal structure of a building. More specifically, here we present a strategy to automatically detect the building levels from a large point cloud obtained with a terrestrial laser scanner survey. The identification of the horizontal planes is the first indispensable step to produce an as-built BIM model. Our algorithm, developed in C++, is based on plane extraction by means of the RANSAC algorithm followed by the minimization of the quadrate sum of points-plane distance. Moreover, this paper will take an in-depth look at the influence of data resolution in the accuracy of plane extraction and at the necessary accuracy for the construction of a BIM model. A laser scanner survey of a three floors building composed by 36 scan stations has produced a point cloud of about 550 million points. The estimated plane parameters at different data resolution are analysed in terms of distance from the full points cloud resolution

Voxel-Based Indoor Reconstruction From HoloLens Triangle Meshes

Current mobile augmented reality devices are often equipped with range sensors. The Microsoft HoloLens for instance is equipped with a Time-Of-Flight (ToF) range camera providing coarse triangle meshes that can be used in custom applications. We suggest to use the triangle meshes for the automatic generation of indoor models that can serve as basis for augmenting their physical counterpart with location-dependent information. In this paper, we present a novel voxel-based approach for automated indoor reconstruction from unstructured three-dimensional geometries like triangle meshes. After an initial voxelization of the input data, rooms are detected in the resulting voxel grid by segmenting connected voxel components of ceiling candidates and extruding them downwards to find floor candidates. Semantic class labels like 'Wall', 'Wall Opening', 'Interior Object' and 'Empty Interior' are then assigned to the room voxels in-between ceiling and floor by a rule-based voxel sweep algorithm. Finally, the geometry of the detected walls and their openings is refined in voxel representation. The proposed approach is not restricted to Manhattan World scenarios and does not rely on room surfaces being planar.Comment: 8 pages, 4 figure

A FLEXIBLE METHODOLOGY FOR OUTDOOR/INDOOR BUILDING RECONSTRUCTION FROM OCCLUDED POINT CLOUDS

Terrestrial Laser Scanning data are increasingly used in building survey not only in cultural heritage domain but also for as-built modelling of large and medium size civil structures. However, raw point clouds derived from laser scanning generally not directly ready for the generation of such models. A time-consuming manual modelling phase has to be taken into account. In addition the large presence of occlusion and clutter may turn out in low-quality building models when state-of-the-art automatic modelling procedures are applied. This paper presents an automated procedure to convert raw point clouds into semantically-enriched building models. The developed method mainly focuses on a geometrical complexity typical of modern buildings with clear prevalence of planar features A characteristic of this methodology is the possibility to work with outdoor and indoor building environments. In order to operate under severe occlusions and clutter a couple of completion algorithms were designed to generate a plausible and reliable model. Finally, some examples of the developed modelling procedure are presented and discussed

Automatic Reconstruction of Parametric, Volumetric Building Models from 3D Point Clouds

Planning, construction, modification, and analysis of buildings requires means of representing a building's physical structure and related semantics in a meaningful way. With the rise of novel technologies and increasing requirements in the architecture, engineering and construction (AEC) domain, two general concepts for representing buildings have gained particular attention in recent years. First, the concept of Building Information Modeling (BIM) is increasingly used as a modern means for representing and managing a building's as-planned state digitally, including not only a geometric model but also various additional semantic properties. Second, point cloud measurements are now widely used for capturing a building's as-built condition by means of laser scanning techniques. A particular challenge and topic of current research are methods for combining the strengths of both point cloud measurements and Building Information Modeling concepts to quickly obtain accurate building models from measured data. In this thesis, we present our recent approaches to tackle the intermeshed challenges of automated indoor point cloud interpretation using targeted segmentation methods, and the automatic reconstruction of high-level, parametric and volumetric building models as the basis for further usage in BIM scenarios. In contrast to most reconstruction methods available at the time, we fundamentally base our approaches on BIM principles and standards, and overcome critical limitations of previous approaches in order to reconstruct globally plausible, volumetric, and parametric models.Automatische Rekonstruktion von parametrischen, volumetrischen Gebäudemodellen aus 3D Punktwolken Für die Planung, Konstruktion, Modifikation und Analyse von Gebäuden werden Möglichkeiten zur sinnvollen Repräsentation der physischen Gebäudestruktur sowie dazugehöriger Semantik benötigt. Mit dem Aufkommen neuer Technologien und steigenden Anforderungen im Bereich von Architecture, Engineering and Construction (AEC) haben zwei Konzepte für die Repräsentation von Gebäuden in den letzten Jahren besondere Aufmerksamkeit erlangt. Erstens wird das Konzept des Building Information Modeling (BIM) zunehmend als ein modernes Mittel zur digitalen Abbildung und Verwaltung "As-Planned"-Zustands von Gebäuden verwendet, welches nicht nur ein geometrisches Modell sondern auch verschiedene zusätzliche semantische Eigenschaften beinhaltet. Zweitens werden Punktwolkenmessungen inzwischen häufig zur Aufnahme des "As-Built"-Zustands mittels Laser-Scan-Techniken eingesetzt. Eine besondere Herausforderung und Thema aktueller Forschung ist die Entwicklung von Methoden zur Vereinigung der Stärken von Punktwolken und Konzepten des Building Information Modeling um schnell akkurate Gebäudemodelle aus den gemessenen Daten zu erzeugen. In dieser Dissertation präsentieren wir unsere aktuellen Ansätze um die miteinander verwobenen Herausforderungen anzugehen, Punktwolken mithilfe geeigneter Segmentierungsmethoden automatisiert zu interpretieren, sowie hochwertige, parametrische und volumetrische Gebäudemodelle als Basis für die Verwendung im BIM-Umfeld zu rekonstruieren. Im Gegensatz zu den meisten derzeit verfügbaren Rekonstruktionsverfahren basieren unsere Ansätze grundlegend auf Prinzipien und Standards aus dem BIM-Umfeld und überwinden kritische Einschränkungen bisheriger Ansätze um vollständig plausible, volumetrische und parametrische Modelle zu erzeugen.</p

Efficient 3D Mapping and Modelling of Indoor Scenes with the Microsoft HoloLens: A Survey

The Microsoft HoloLens is a head-worn mobile augmented reality device. It allows a real-time 3D mapping of its direct environment and a self-localisation within the acquired 3D data. Both aspects are essential for robustly augmenting the local environment around the user with virtual contents and for the robust interaction of the user with virtual objects. Although not primarily designed as an indoor mapping device, the Microsoft HoloLens has a high potential for an efficient and comfortable mapping of both room-scale and building-scale indoor environments. In this paper, we provide a survey on the capabilities of the Microsoft HoloLens (Version 1) for the efficient 3D mapping and modelling of indoor scenes. More specifically, we focus on its capabilities regarding the localisation (in terms of pose estimation) within indoor environments and the spatial mapping of indoor environments. While the Microsoft HoloLens can certainly not compete in providing highly accurate 3D data like laser scanners, we demonstrate that the acquired data provides sufficient accuracy for a subsequent standard rule-based reconstruction of a semantically enriched and topologically correct model of an indoor scene from the acquired data. Furthermore, we provide a discussion with respect to the robustness of standard handcrafted geometric features extracted from data acquired with the Microsoft HoloLens and typically used for a subsequent learning-based semantic segmentation

Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd

Indoor Mapping and Reconstruction with Mobile Augmented Reality Sensor Systems

Augmented Reality (AR) ermöglicht es, virtuelle, dreidimensionale Inhalte direkt innerhalb der realen Umgebung darzustellen. Anstatt jedoch beliebige virtuelle Objekte an einem willkürlichen Ort anzuzeigen, kann AR Technologie auch genutzt werden, um Geodaten in situ an jenem Ort darzustellen, auf den sich die Daten beziehen. Damit eröffnet AR die Möglichkeit, die reale Welt durch virtuelle, ortbezogene Informationen anzureichern. Im Rahmen der vorliegenen Arbeit wird diese Spielart von AR als "Fused Reality" definiert und eingehend diskutiert. Der praktische Mehrwert, den dieses Konzept der Fused Reality bietet, lässt sich gut am Beispiel seiner Anwendung im Zusammenhang mit digitalen Gebäudemodellen demonstrieren, wo sich gebäudespezifische Informationen - beispielsweise der Verlauf von Leitungen und Kabeln innerhalb der Wände - lagegerecht am realen Objekt darstellen lassen. Um das skizzierte Konzept einer Indoor Fused Reality Anwendung realisieren zu können, müssen einige grundlegende Bedingungen erfüllt sein. So kann ein bestimmtes Gebäude nur dann mit ortsbezogenen Informationen augmentiert werden, wenn von diesem Gebäude ein digitales Modell verfügbar ist. Zwar werden größere Bauprojekt heutzutage oft unter Zuhilfename von Building Information Modelling (BIM) geplant und durchgeführt, sodass ein digitales Modell direkt zusammen mit dem realen Gebäude ensteht, jedoch sind im Falle älterer Bestandsgebäude digitale Modelle meist nicht verfügbar. Ein digitales Modell eines bestehenden Gebäudes manuell zu erstellen, ist zwar möglich, jedoch mit großem Aufwand verbunden. Ist ein passendes Gebäudemodell vorhanden, muss ein AR Gerät außerdem in der Lage sein, die eigene Position und Orientierung im Gebäude relativ zu diesem Modell bestimmen zu können, um Augmentierungen lagegerecht anzeigen zu können. Im Rahmen dieser Arbeit werden diverse Aspekte der angesprochenen Problematik untersucht und diskutiert. Dabei werden zunächst verschiedene Möglichkeiten diskutiert, Indoor-Gebäudegeometrie mittels Sensorsystemen zu erfassen. Anschließend wird eine Untersuchung präsentiert, inwiefern moderne AR Geräte, die in der Regel ebenfalls über eine Vielzahl an Sensoren verfügen, ebenfalls geeignet sind, als Indoor-Mapping-Systeme eingesetzt zu werden. Die resultierenden Indoor Mapping Datensätze können daraufhin genutzt werden, um automatisiert Gebäudemodelle zu rekonstruieren. Zu diesem Zweck wird ein automatisiertes, voxel-basiertes Indoor-Rekonstruktionsverfahren vorgestellt. Dieses wird außerdem auf der Grundlage vierer zu diesem Zweck erfasster Datensätze mit zugehörigen Referenzdaten quantitativ evaluiert. Desweiteren werden verschiedene Möglichkeiten diskutiert, mobile AR Geräte innerhalb eines Gebäudes und des zugehörigen Gebäudemodells zu lokalisieren. In diesem Kontext wird außerdem auch die Evaluierung einer Marker-basierten Indoor-Lokalisierungsmethode präsentiert. Abschließend wird zudem ein neuer Ansatz, Indoor-Mapping Datensätze an den Achsen des Koordinatensystems auszurichten, vorgestellt