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Odometer information is an important feature of NDT systems for inspection procedures that involve mechanical scanning. Knowledge of transducer position during the inspection allows for the localization of flaws and the fusion (stitching) of several images into more sophisticated representations of the inspected object. Commercial encoders provide NDT systems with accurate realtime displacement information that can be integrated to obtain odometry. However, this information is typically limited to a single axis. Although composite schemes with more than one encoder can be built to provide 2-D or 3-D spatial information, they are mechanically intricate and lack flexibility and ease of use. We propose a 2-D position-tracking solution that is based on image processing. A miniature camera continuously captures images of the external surface of the inspected object, which are fed to an algorithm that detects and stores 2-D displacement between each pair of consecutive images. Additionally, the orientation quaternions provided by an Inertial Measurement Unit are stored, allowing for posterior 3-D path reconstruction over objects of known geometries, such as oil pipes. Besides logging position and orientation histories, the device also provides real-time displacement information to the NDT system, where it is perceived as a set of single-axis encoders, thus termed “the virtual encoder”. We demonstrate the applicability of the device to both contact and immersion ultrasonic inspections. The results show that the concept is promising, despite being based on simple principles and relatively easy to implement. The source code is provided as additional material in https://github.com/thiagokalid/VirtualEncoder-ECNDT-2023.

Stereoscopic cameras, also known as depth cameras, are increasingly present in robotics navigation and 3D scene reconstruction. Normally, off-the-shelf depth cameras are not suited for underwater environments and exhibit considerable distortions in the provided 3D images when operating underwater. This is mainly due to the large difference in the refractive coefficients of air and water. This issue prevents their use in subsea applications such as the inspection of oil pipelines, valves and manifolds, where these devices could be used for odometry during ultrasonic inspections as well as for 3D scanning. Aiming to allow for such applications, we present a data-driven method for the correction of distortions caused by unaccounted diffractions in depth cameras. First, the distortion is modelled via a series of detections of control points from a standard target at known underwater positions, and an approximation of the inverse distortion is obtained. Then, at runtime, a procedure takes as input the (distorted) point cloud given by the camera at each frame, applies the inverse distortion, and yields a corrected point cloud. We apply the method to data provided by a RealSense D405 camera encased in a sealed acrylic container underwater. The correction makes it possible to use the camera for odometry underwater, clearing the path for the use of off-the-shelf depth cameras in a wide range of subsea applications. The method can be applied for any other transparent fluid.