Evaluation of Parameters that Impact Data Collection for Robot Progress Detection of a Masonry Wall

The construction industry is going through a technological revolution where aspects of digitalization are combined with automation. A prime example is using data from a building information model (BIM) to promote robotic construction. However, numerous parameters impact the robot’s ability to collect data and provide user feedback for site progress detection. For instance, during progress detection, the robot must recognize site work features, such as a masonry wall. Therefore, this study investigates four parameters that would impact the data collection process for progress detection, including wall configuration, robot navigation path, image spacing, and distance from the wall. Photogrammetry is used to capture images with a teleoperated Husky A200 robot equipped with a stereo camera and the Global Positioning System (GPS). COLMAP was used to generate both the sparse and dense point cloud reconstruction and statistics related to the model, such as the reconstruction time, mean reprojection error, percentage of images used for reconstruction, and points generated. Once COLMAP processed all reconstructions, each model was reviewed individually. The results were filtered to determine viable methods for the data collection process and provide insight into which methods required further analysis to ascertain the quality of the model. However, the main goal of this study is to document and investigate the methods used in the data collection process while determining which methods should facilitate the optimal data collection process.