Automated approach for indoor geometric quality inspection data collection based on BIM and quadruped robot equipped with LiDAR

Indoor geometric quality inspection plays a crucial role in construction quality control. Light detection and ranging (LiDAR) can obtain point cloud data of the indoor environments in full range, which then can be utilized to efficiently extract geometric features of building elements for inspection. However, existing data collection of indoor environments using LiDAR is still manually planned and implemented, which is quite time-consuming as the scanning space is usually unknown and the movability of equipment is limited. Therefore, this paper proposes an automated approach for indoor geometric quality inspection data collection based on Building Information Model (BIM) and quadruped robot equipped with LiDAR. First, BIM with accurate geometric and semantic information of building is integrated with heuristic algorithm to automate scan planning, including scan stations and order. Second, Simultaneous Localization and Mapping (SLAM) integrates with BIM is equipped into a quadruped robot to achieve automated data collection capability. Finally, an intelligent execution module for scanning point cloud data in indoor environments is introduced. The proposed approach is validated in real indoor environments. Compared with traditional data collection methods, the experiment results show that the proposed approach can save 42% of scanning time, and the scanned point cloud data has better quality and enough density, which significantly improves the efficiency of inspection data collection for indoor geometric quality management. By unifying BIM-driven planning, SLAM-based localization, and quadruped robot mobility into a single framework, this study introduces a novel approach for large-scale automated indoor inspection, with attention to connected areas to ensure continuity across multiple spaces.