Integrating 3D Computer Vision and Robotic Infrastructure Inspection

Abstract

Over the past decade, there has been growing interest in the field of robotic inspection systems, in particular Unmanned Aerial Vehicles (UAV). These systems afford many advantages in terms of reducing the cost of condition assessments and expediting the data acquisition while improving the monitoring process efficiency. However, robotic technologies are a means of collecting data, and data collection protocols must be tightly linked with the desired spatial data resolution and final products, particularly if integration with more conventional structural health monitoring technologies is to be attained. This process involves planning a set of views, physically altering the relative structureimaging sensor pose, globally registering all the acquired imagery data, and finally integrating different sources of data into a nonredundant model. In this context, this paper demonstrates the capabilities of using one such integration at the Brighton Dam, a complex and large-scale gravity dam in Maryland, USA. The goal of this study was to evaluate a robotic inspection methodology along with utilizing computer vision-based data analytics on photogrammetrically generated three-dimensional (3D) models of the dam. A series of experiments involving the analysis of both simulated and real structural damage were designed and performed. During these experiments, digital image data was collected from a variety of sources, including both fixed-wing and multi-rotor UAVs, as well as more conventional imaging platforms. These data sources were integrated and merged together to form a detailed high-resolution 3D model of the dam and surrounding environs, with varying level of details in damage and deterioration. 3D data processing techniques were then used to analyze these models to automatically detect and quantify various damages. The results of these tests reflect the capabilities of these powerful new monitoring techniques and indicate that a suite of vision-based algorithms is necessary for comprehensive analyses of these complex 3D models to reliably, and systematically detect damages. The findings of this study also reinforce the critical need for an in advance detailed planning along with team coordination during field data collection.