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Augmented reality (AR) systems are becoming next-generation technologies to intelligently visualize the real world in 3D. This research proposes a sensor fusion based pipeline inspection and retrofitting for the AR system, which can be used in pipeline inspection and retrofitting processes in industrial plants. The proposed methodology utilizes a prebuilt 3D point cloud data of the environment, real-time Light Detection and Ranging (LiDAR) scan and image sequence from the camera. First, we estimate the current pose of the sensors platform by matching the LiDAR scan and the prebuilt point cloud data from the current pose prebuilt point cloud data augmented on to the camera image by utilizing the LiDAR and camera calibration parameters. Next, based on the user selection in the augmented view, geometric parameters of a pipe are estimated. In addition to pipe parameter estimation, retrofitting in the existing plant using augmented scene are illustrated. Finally, step-by-step procedure of the proposed method was experimentally verified at a water treatment plant. Result shows that the integration of AR with building information modelling (BIM) greatly benefits the post-occupancy evaluation process or pre-retrofitting and renovation process for identifying, evaluating, and updating the geometric specifications of a construction environment. [ABSTRACT FROM AUTHOR]

Human eyeballs move relative to the head, resulting in optimal changes in the viewpoint. We tested similar vestibular ocular reflex (VOR)-based movement on Zenmuse-X3 gimbal camera relative to pre-defined YAW movements of the DJI Matrice-100 unmanned aerial vehicle (UAV). Changes in viewpoint have various consequences for visual and graphical rendering. Therefore, this study investigated how to minimize these changes. OpenGL visualization was performed to simulate and measure viewpoint changes using the proposed VOR-based eyeball movement algorithm and compared with results of VOR based gimbal movement. The gimbal camera was setup to render images (scenes) on flat monitors. Positions of pre-fixed targets in the images were used to measure the viewpoint changes. The proposed approach could successfully control and significantly reduce the viewpoint changes and stabilize the image to improve visual tracking of targets on flat monitors. The proposed method can also be used to render real-time camera feed to a head-mounted display (HMD) in an ergonomically pleasing way. [ABSTRACT FROM AUTHOR]

This study proposed a gaze-controlled method for visualization, navigation, and retrofitting of large point cloud data (PCD), produced by unmanned aerial vehicles (UAV) mounted with laser range-scanners. For this purpose, the estimated human gaze point was used to interact with a head-mounted display (HMD) to visualize the PCD and the computer-aided design (CAD) models. Virtual water treat plant pipeline models were considered for retrofitting against the PCD of the actual pipelines. In such an application, the objective was to use the gaze data to interact with the HMD so the virtual retrofitting process was performed by navigating with the eye gaze. It was inferred that the integration of eye gaze tracking for visualization and interaction with the HMD could improve both speed and functionality for human–computer interaction. A usability study was conducted to investigate the speed of the proposed method against the mouse interaction-based retrofitting. In addition, immersion, interface quality and accuracy was analyzed by adopting the appropriate questionnaire and user learning was tested by conducting experiments in iterations from participants. Finally, it was verified whether any negative psychological factors, such as cybersickness, general discomfort, fatigue, headache, eye strain and difficulty concentrating through the survey experiment. [ABSTRACT FROM AUTHOR]