Underwater Sea-floor Mapping for a Robotic Application, Using Visual Information

The underwater environment is extremely wide and diverse. Conducting underwater surveys proves to be valuable for numerous scientific applications in the fields of archaeology, geology and biology, among others, involving tasks like shipwreck prospections, ecological studies, environmental damage assessment, and detection of temporal changes of the sea-floor. Nowadays, such time-consuming and environment specific tasks are typically carried out by Underwater Vehicles, which can be equipped with multiple sensors, including optical cameras. These cameras provide high resolution visual information of the sea-floor which can be used for detailed inspection and study. The underwater medium, however, presents some additional challenges to the image acquisition task, namely, physical phenomena such as light attenuation and scattering. These phenomena are intensified over larger distances, forcing image acquisition to be performed as close to the seafloor as possible. In order to generate wide area optical maps of the sea-floor, an image mosaic has to be built by combining multiple small-area pictures. This dissertation presents the developed work on an algorithm aimed at solving that problem, in order to produce a large-scale optical map of the sea-floor. First, a short introduction is presented, with the context and motivation for this work, as well as the proposed goals – properly calibrate a camera in the underwater environment; develop and implement a robust image mosaicking algorithm; and validate the algorithm with real sea-floor data. The main state-of-the-art topics related to optical mapping are then explored, with emphasis on the acquisition sensors, the limitations of light propagation in the underwater medium, some image preprocessing techniques and other works on image mosaicking. An overview of the most relevant theoretical principals and background information is presented, regarding the camera model, two-view geometry of a scene and its constraints, image preprocessing steps, feature detectors, global alignment techniques and image stitching strategies. Finally, the developed work is fully explained and validated with real data. At first in smallscale and in controlled conditions inside the laboratory’s water tank, and then with real imagery, using a sea-floor dataset. A translation correction algorithm is presented, which added the capability of inserting images into the mosaic in any direction. A new blending algorithm is also explored and proved to be efficient at minimizing unavoidable parallax effects. A comparison between simple average blending and the developed algorithm with different threshold levels is also presented. And finally, a new large-scale image mosaic approach is presented and validated with the real dataset, using batches of sequential images for demonstration purposes. The developed work proved to be able to meet the proposed goals, providing a reliable optical map of most of the sea-floor imagery used. However, some limitations were identified – significant parallax effects, present in non-planar surfaces like the algae-rocky area of the used dataset, demonstrate that the proposed work is incapable of overcoming such issues. For this kind of situation, a different architecture would have to be used, with resource to additional navigational information and/or global alignment techniques.