Slam and Beacon Data for Automation of Indoor Construction Progress Tracking

Abstract

Abstract Construction progress tracking and monitoring is a complex process that is crucial for the successful execution of projects and the delivery of a high-quality product to the client. However, these tasks remain mostly manual - time-consuming and error prone, which often leads to suboptimal quality, cost and schedule overruns. The present research proposes the use of an autonomous rover for the data collection from construction site. The objective is to create a hybrid data processing system using point clouds from the Simultaneous Localization and Mapping (SLAM) algorithm used for robot navigation, and beacons’ data integrated with BIM, to track construction progress and provide project managers with reliable information in quasireal time. The paper is composed of four parts: first, a literature review of best practices regarding the technology used for progress tracking is performed. Second, we propose a framework for automated data collection and information processing for automated progress tracking and monitoring. Third, we present a real-world case study partially implementing the framework by using data acquired by an autonomous rover and BIM, and simulate a real-time reconstruction of the construction site status. Finally, the results are discussed, and future work is identified.