The Virtual Interactive Relationship Between BIM Project Teams: Effective Communication to aid Collaboration in the Design Process

Building Information Modelling (BIM) provides a shared source of information about a built asset, which creates a collaborative virtual environment for project teams. Literature suggests that to collaborate efficiently, the relationship between the project team is based on sympathy, obligation, trust and rapport. Communication increases in importance when working collaboratively but effective communication can only be achieved when the stakeholders are willing to act, react, listen and share information. Case study research and interviews with Architecture, Engineering and Construction (AEC) industry experts suggest that synchronous face-to-face communication is project teams’ preferred method, allowing teams to socialise and build rapport, accelerating the creation of trust between the stakeholders. However, virtual unified communication platforms are a close second-preferred option for communication between the teams. Effective methods for virtual communication in professional practice, such as virtual collaboration environments (CVE), that build trust and achieve similar spontaneous responses as face-to-face communication, are necessary to face the global challenges and can be achieved with the right people, processes and technology. This research paper investigates current industry methods for virtual communication within BIM projects and explores the suitability of avatar interaction in a collaborative virtual environment as an alternative to face-to-face communication to enhance collaboration between design teams’ professional practice on a project. Hence, this paper presents comparisons between the effectiveness of these communication methods within construction design teams with results of further experiments conducted to test recommendations for more efficient methods for virtual communication to add value in the workplace between design teams.

Entornos virtuales: predicción de la satisfacción en contexto universitario

El presente estudio persigue un doble objetivo: evaluar el grado de satisfacción de los estudiantes con la formación recibida en un entorno virtual y, analizar su capacidad predictiva sobre la satisfacción. Se ha utilizado la versión española del cuestionario Distance Education Learning Environments Survey (Sp-DELES). Los resultados ponen de manifiesto el significativo nivel de satisfacción de los estudiantes con la experiencia y revelan las variables más importantes a la hora de explicar la varianza en satisfacción.

3D plane-based egomotion for SLAM on semi-structured environment

Several works deal with 3D data in SLAM problem. Data come from a 3D laser sweeping unit or a stereo camera, both providing a huge amount of data. In this paper, we detail an efficient method to extract planar patches from 3D raw data. Then, we use these patches in an ICP-like method in order to address the SLAM problem. Using ICP with planes is not a trivial task. It needs some adaptation from the original ICP. Some promising results are shown for outdoor environment.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.