A Mosaicking Approach for Visual Mapping of Large-Scale Environments

Abstract

Nowadays, the technological and scientific research related to underwater perception is focused in developing more cost-effective tools to support activities related with the inspection, search and rescue of wreckages and site exploration: devices with higher autonomy, endurance and capabilities. Currently, specific tasks are already carried out by remotely-operated vehicles (ROV) and autonomous underwater vehicles (AUV) that can be equipped with multiple sensors, including optical cameras which are extremely valuable for perceiving marine environments; however, the current perceptual capability of these vehicles is still limited. In this context, the paper presents a novel mosaicking method that composes the sea-floor from a set of visual observations. This method is called RObust and Large-scale MOSaicking (ROLAMOS) and it enables an efficient frame-to-frame motion estimation with outliers removal and consistence checking, a robust registration of monocular images and, finally, a mosaic management methodology that makes it possible to map large visual areas with a high resolution. The experiments conducted with realistic images have proven that the ROLAMOS is suitable for mapping large-scale sea-floor scenarios because the visual information is registered by managing the computational resources that are available onboard, which makes it appropriate for applications that do not have specialized computers. Further, this is a major advantage for automatic mosaic creation based on robotic applications, that require the location of objects or even structures with high detail and precision.