Automated Progress Monitoring Using Mixed Reality

This thesis presents a real-time automated building progress monitoring solution for indoor environments using a mobile device. Such a system could prompt accurate and timely assessment of work progress that would allow managers to make adjustments and minimise both time and cost overrun when deviations from the schedule occur. Although many researchers have proposed approaches for progress monitoring in outdoor scenes, these cannot perform in real-time and shift into the complex interior environment. Research efforts for indoor environments are not fully automated and lead to errors in more complex scenes. Systems based on mobile devices could potentially enhance the inspection process and reduce the required time by allowing the inspector to acquire progress data by simply walking around the site. The main challenge of these systems is the tracking of the pose of the camera to achieve accurate alignment between the 3D design model and the real-world scene. Methods for estimating the user’s pose rely on a) tags on each target of interest, which require additional time and cost for installation and maintenance; b) pre-selected user locations, which restricts the user to those locations only; or c) GPS on the augmented reality headset, which only applies to outdoor inspections. Additionally, current mobile-based inspection systems do not perform any comparison between the captured as-built and the as-planned data. In this research, different marker-less Augmented Reality (AR) potential methods were implemented and tested for finding the most robust tracking solution. The Microsoft HoloLens was found to be the top performer for tracking the user’s pose and for overall user-experience. Next, a semi-automated method was developed for initially registering the 3D model to the real environment by exploiting information from detected floor and wall surfaces. Results showed that this method reduces the time of the initial registration by 58%. Having the 3D model aligned to the real environment and knowing the pose of the camera at every moment, an automated method was developed that exploits the captured as-built surface mesh data from the mobile device, compares it against the 3D design model and identifies in real-time whether an object has been built according to plan. Different parameters were tested for finding the optimum combination based on the current quality of mesh data. If quality of mesh data changes, then new parameters should be explored. Finally, the proposed solution was tested in real site conditions resulting in 76.6% precision, 100.0% recall, and 83.5% accuracy

Real Time Volume to Plane Comparison for Mixed-Reality based Progress Monitoring

On-site construction inspection for progress monitoring is important since accurate and timely assessment of progress allows managers to make adjustments to minimise time and cost overrun when deviations from the schedule occur. However, current practice still remains manual, time consuming and labour intensive which usually requires exhaustive manual data extraction from drawings and other databases. Inspection of the interior environment can be even more challenging due to the complexity and interconnectivity of tasks. Systems based on mobile devices could potentially enhance the inspection process and reduce the required time by allowing the inspector to acquire progress data by simply walking around the site. This paper presents an automated method that allows a real time comparison between the registered as-built and as-planned data while the inspector navigates inside a building under construction to perform inspection wearing Microsoft HoloLens device. Proposed method automatically detects if volumetric objects such as beams, columns, walls etc. exist in reality according to plan. Different parameters of the method were tested and 95.2% accuracy, 96.5% precision and 97.3% recall was achieved by using the optimum set of parameters.European Commission FP

BIM Registration Methods for Mobile Augmented Reality-Based Inspection

On-site construction inspection for progress monitoring is a manual, time consuming and labour intensive pro-cess consumed by exhaustive manual extraction of data from drawings and databases. Efforts have been made to facilitate the inspection process by using emerging technologies such as Augmented Reality (AR). AR based systems can simplify and reduce the time of inspection by providing the inspector with instantane-ous access to the information stored in the Building Information Modelling (BIM). However, precise align-ment between the BIM model and the real world scene is still a challenge. For estimating the position and ori-entation of the user, methods have been proposed that either use markers or confine the user to a specific loca-tion, or use Global Positioning System (GPS) which cannot operate efficiently in an indoor environment. This paper presents an evaluation of different methods that could potentially be used for a marker-less BIM regis-tration in AR. We implemented and tested line, edge, and contour detection algorithms using images, data from LSD and ORB Simultaneous Localisation and Mapping (SLAM) methods and 3D and positioning data from Kinect sensor and Google Project Tango. The results indicate that sparse 3D data is the input dataset that leads to the most robust results when combined with XYZ method