Abdel-Wahab, Mohamed

Bosché, Frédéric

Carozza, Ludovico

English

Heriot Watt Pure

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Image-based localization for an indoor VR/AR construction training system

Virtual /Augmented Reality (VR/AR) technologies have been increasingly used in recent years to support different areas of the construction industry. Its simulation capabilities can enable different construction stakeholders to evaluate the impact of their choices not only on the built environment, but also with regard to the correct execution of operational procedures. Training providers, such as Further Education (FE) colleges, can also enhance their trainee’s experience through the simulation of realistic construction contexts whilst eliminating health and safety risks. Current approaches for the simulation of learning environments in Construction, such as Virtual Learning Environment (VLEs), provide limited degree of interactivity during the execution of real working tasks. Whilst immersive approaches (e.g. CAVE-based) can provide enhanced visualization of simulated environments, they require complex and expensive set-up with limited practical interaction in real construction projects context.This paper outlines a localization approach employed in the development of an Immersive Environment (IE) for Construction training, cheaper than CAVE-based approaches and with the potential to be rolled-out widely across the FE sector for maximizing the benefit to the construction industry. Pose estimation of the trainee is achieved by processing images acquired by a monocular camera integral with his head while performing tasks in a virtual construction environment. Realistic perception of the working environment and its potentially hazardous conditions can thus be consistently delivered to the trainee through immersive display devices (e.g. goggles). Preliminary performance of the localization approach is reported in the context of working at heights (which has a wide applicability to a range of construction trades, such as scaffolders and roofers), whilst highlighting the potential benefits for trainees. Current limitations of the localization approach are also discussed suggesting directions for future development

Image-based localization for an indoor VR/AR construction training system

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Heriot Watt Pure

Heriot Watt Pure