In an effort to increase situational awareness, the aviation industry is investigating technologies that allow pilots to visualize what is outside of the aircraft during periods of low-visibility. One of these technologies, referred to as Synthetic Vision Systems (SVS), provides the pilot with real-time computer-generated images of obstacles, terrain features, runways, and other aircraft regardless of weather conditions. To help ensure the integrity of such systems, methods of verifying the accuracy of synthetically-derived display elements using onboard remote sensing technologies are under investigation. One such method is based on a shadow detection and extraction (SHADE) algorithm that transforms computer-generated digital elevation data into a reference domain that enables direct comparison with radar measurements. This paper describes machine vision techniques for making this comparison and discusses preliminary results from application to actual flight data.
The National Aeronautics and Space Administration (NASA), other government agencies, and private industry have requirements to map and analyze 3-dimensional surfaces of varying regularity and material composition. This requires a high fidelity, 3-dimensional description of the work space. In cases where complete and current information about the space is not available, topographic characterization allows on-line initialization and/or modification of the work space database. The mapping environment is often challenging with regard to lighting, radiation, temperature, atmosphere, and causticity. This paper describes a system that provides topographic characterization based on fusing intensity and depth information, and describes the application of this technique for inspection of Shuttle thermal tiles.
The description, analysis, and experimental results of a method for identifying possible defects on high temperature reusable surface insulation (HRSI) of the Orbiter thermal protection system (TPS) is presented. Currently, a visual postflight inspection of Orbiter TPS is conducted to detect and classify defects as part of the Orbiter maintenance flow. The objective of the method is to automate the detection of defects by identifying anomalies between preflight and postflight images of TPS components. The initial version is intended to detect and label gross (greater than 0.1 inches in the smallest dimension) anomalies on HRSI components for subsequent classification by a human inspector. The approach is a modified Golden Template technique where the preflight image of a tile serves as the template against which the postflight image of the tile is compared. Candidate anomalies are selected as a result of the comparison and processed to identify true anomalies. The processing methods are developed and discussed, and the results of testing on actual and simulated tile images are presented. Solutions to the problems of brightness and spatial normalization, timely execution, and minimization of false positives are also discussed.
Accomplishing a task with telerobotics typically involves a combination of operator control/supervision and a `script' of preprogrammed commands. These commands usually assume that the location of various objects in the task space conform to some internal representation (database) of that task space. The ability to quickly and accurately verify the task environment against the internal database would improve the robustness of these preprogrammed commands. In addition, the on-line initialization and maintenance of a task space database is difficult for operators using Cartesian coordinates alone. This paper describes the interactive scene analysis module (ISAM) developed to provide task space database initialization and verification utilizing 3-D graphic overlay modeling, video imaging, and laser radar based range imaging. Through the fusion of task space database information and image sensor data, a verifiable task space model is generated providing location and orientation data for objects in a task space. This paper also describes applications of the ISAM in the Intelligent Systems Research Laboratory (ISRL) at NASA Langley Research Center, and discusses its performance relative to representation accuracy and operator interface efficiency.