Automatic Wall Defect Detection Using an Autonomous Robot: A Focus on Data Collection

Abstract

Detection of wall defects by autonomous robots enhances building inspection with reduced labor, improved productivity, and inspection accuracy, in comparison with manual inspection. Accordingly, various advanced technology-enabled techniques have been investigated to detect defects through collected images. However, rare effort has been put on developing an automatic data collection system to support the collected data with high quality (complete but not redundant). In this paper, an autonomous robot-enabled data collection system is developed for indoor wall condition inspection. The autonomous robot is equipped with sensors for navigation, map building, and obstacle avoidance. To generate safer, more reasonable collision-free wall-following trajectories, improved heuristic algorithms are used to optimize robot trajectory. The developed data collection system with navigation of an autonomous robot is evaluated by simulation. The obtained results indicate that the hybrid approach for the automatic data collection system is able to collect all available walls and corners without redundant trajectories.