Vessel maintenance entails periodic visual inspections of internal and external parts of the hull in order to detect the typical defective situations affecting metallic structures. Nowadays, robots are becoming more and more important regarding these inspection tasks, since they can collect the requested information and, thus, prevent humans from performing tedious, and even dangerous tasks because of places hard to reach for humans. A Micro Aerial Vehicle (MAV) fitted with vision cameras can be used as part of an automated or semi-automated inspection strategy. The resulting collection of individual images, however, does not permit the surveyor to get a global overview of the state of the surface under inspection, apart from the fact that typically defects appear broken along a number of consecutive images. Image mosaicing can certainly help in this case. To this end, in this paper, we propose a novel image mosaicing approach able to deal with this kind of scenarios. Our solution employs a graph-based registration method from which relevant topological relationships between (overlapping) images are found. This graph is built according to a visual index based on a Bag-of-Words (BoW) scheme making use of binary descriptors for speeding up the image description process. At the end of the paper, we report about the results of a number of experiments that validate our approach, including the outcome of defect detectors working directly over the mosaic.