A sematic and prior-knowledge-aided monocular localization method for construction-related entities

The real-time location of construction-related entities is some of the most useful basic information for automated construction management. However, the implementation of most existing localization methods is limited due to the weak adaptability to construction sites. In this paper, we enhance the monocular vision technique for the localization of construction-related entities by a sematic and prior knowledge-based method. A deep learning algorithm is employed to segment the sematic instance in the images, and then the prior knowledge model specifies projection strategies for entities corresponding to various scenarios. Results show that the proposed method achieves satisfying positioning accuracy, is robust in low-ratio occlusions, and can help facilitate safety early warning, activity recognition, and productivity analysis.