Autonomous 3D vision-based bolt loosening assessment using micro aerial vehicles

Abstract

Earlier identification of bolt loosening is crucial to maintain structural integrity and prevent system-level collapse. In this study, a novel drone-based 3D vision methodology has been proposed for autonomous bolt loosening assessment. First, a low-cost micro aerial vehicle with various types of sensors is designed. Second, a drone-based autonomous image collection method is proposed. Third, a 3D point cloud of the bolted connection is generated using the acquired images. Fourth, 3D point cloud processing methods are proposed to localize and quantify bolt loosening. The proposed method has been implemented on structural beam–column connections. The results show that the proposed drone-based data collection method can effectively acquire images for 3D reconstruction. The 3D point cloud processing methods can reliably localize and quantify bolt loosening at high accuracy. The proposed method provides a more robust and comprehensive evaluation of bolt loosening, compared to existing 2D vision methods, which process 2D images captured at a specific camera view.