13. Measuring Full-Field Deformation in Ultra-High-Performance Concrete Structural Components Using Tag-Based Robotic Vision

In the past few years, computer vision-based applications are widely used to determine damages in structural components. However, due to the involvement of certain levels of manual measurements, there still exists potential for improvements in the quantification of damage caused in a structure. Therefore, in this study, key-point point detection using fiducial markers is used to reduce the error in quantification of overall displacement and deformed profile of structural components (e.g., beams). This study demonstrates the use of a fiducial marker-based approach to quantify the deformation and bending profile in beam elements without physically disturbing the specimen. The visual fiducial marker (e.g., AprilTag) system has been extensively used in robotics for purposes ranging from localization of robots to increasing precision while tagging different objects in the robot’s surroundings. These AprilTag are attached to the surface of the specimen before loading. In this research, while the load is applied on the beam, the behavior of the specimen is then recorded by using a high-resolution digital camera during the experiment. Camera calibration using a checkerboard is performed to determine the extrinsic parameters (e.g., camera pose). With the help of attached AptilTags on the surface of the specimen, pixel values at the four corners and the center of each of the tags are detected during the experiment. These pixel locations are then used to get the corresponding world coordinate system by minimizing the loss function. Unlike normal bundle adjustment algorithms, camera parameters are treated as known parameters during the optimization process. Primary results show potential of proposed method in obtaining robust measurements.