# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 7 Discussion
### Challenges and Future Works
#### 7.2.2 Mapping between Videos and the Real Construction Site  
Vision-based positioning is in the early stage for application to construction safety. Researchers need to increase their effort into tackling the challenges, including occlusion, clutter, illumination, and scale variation [99]. However, the critical challenge faced by the implementation of vision-based positioning is mapping video being captured by a camera and the actual location of the construction site. Several of the reviewed studies used machine learning to collect information from images to determine the location of a worker or an object. However, unlike a sensor that can directly obtain the exact position, image-based positioning only provides a relative position. This position is the coordinate relative to a point (upper left corner) of the screen rather than the actual location of the object on the construction site, so it is impossible to estimate the danger outside the field of vision. Therefore, the information from image-based positioning needs further data processing for the synchronisation with construction activities.  
There are three issues for information processing. The first is mapping the local coordinates in the video with the actual position of the workplace. Existing studies have simplified the construction site into a plane to map the local coordinates and the actual location. However, many slopes and pits exist on a construction site, so it is necessary to synchronise the complex terrain on the site first, and then establish the mapping between the coordinate in the video and the position in the real world. The second issue is that the terrain of the site changes gradually as construction progresses. The synchronisation of the terrain should also be updated as the project progresses. The last issue is the integration of object positioning information. When multiple cameras capture the same object at the same time, an object detection algorithm recognises the object separately from each video, providing duplicate information. In a VCS, however, the algorithms need to accurately determine that there is only one object at that location. These three issues involve multi-disciplinary fields such as 4D BIM, lens distortion, object detection, vibration control, topology, and stochastic degradation [74; 89; 100; 101; 102]. A wider range of knowledge is needed to break through the current bottleneck.  
Future works dealing with the positioning issues are most likely to introduce simultaneous localisation and mapping (SLAM). SLAM was originally used to solve the localisation, navigation, and mapping construction problems of mobile robots running in an unknown environment. For constantly changing construction sites, workers can use SLAM to automatically update the environment on the construction site in real-time. Regarding the devices, many electrical devices such as mobile phones and AR HMD already have time of flight (ToF) or light detection and ranging (LiDAR) modules, which form the hardware base of SLAM. The introduction of SLAM would mitigate the former two issues. Therefore, the third issue will become the bottleneck.