Reinforcement learning for high-quality reality mapping of indoor construction using unmanned ground vehicles.

Recent advances in reality capture technology focused on automating reality capture and devising robust computational models to convert the collected data into usable formats. However, these modern approaches are still challenged by the insufficient capacity of the resulting information to relay accurate and complete representations of the construction state due to the data's poor visual quality. In addition, the complexity of robotic path planning –especially for indoor construction– hinders automatic visual data collection due to cluttered, narrow, and dynamic construction spaces. This work targets both challenges by presenting a reinforcement learning model that optimizes indoor data collection policy for acquiring high-quality visual data using camera-equipped unmanned ground rovers. Results from three learned navigation policies show the capability of the method to provide high visual quality for the collected data. The learned policies reduced the data collection duration by 38.23% on average compared to the currently used automatic data collection strategies. The policies also provided a 31.04% average reduction in data collection distance compared to lawn-mower patterns. [Display omitted] • Reinforcement learning is used to learn a navigation policy maximizing data's visual quality and minimizing capture duration. • The optimized navigation policies are evaluated for data's visual quality and safe data collection navigation. • Divide-and-conquer algorithm reduces the optimization's computational complexity providing solutions in a timely manner. • Learnt capture plans achieve maximum visual quality in less duration than traditional collection plans. • Reinforcement learning provides ∼200% improvement in data collection duration compared to state-of-the-art NPV methods. [ABSTRACT FROM AUTHOR]