3d Sequential Image Mosaicing for Underwater Navigation and Mapping

Abstract

Abstract. Although fully autonomous mapping methods are becoming more and more common and reliable, still the human operator is regularly employed in many 3D surveying missions. In a number of underwater applications, divers or pilots of remotely operated vehicles (ROVs) are still considered irreplaceable, and tools for real-time visualization of the mapped scene are essential to support and maximize the navigation and surveying efforts. For underwater exploration, image mosaicing has proved to be a valid and effective approach to visualize large mapped areas, often employed in conjunction with autonomous underwater vehicles (AUVs) and ROVs. In this work, we propose the use of a modified image mosaicing algorithm that coupled with image-based real-time navigation and mapping algorithms provides two visual navigation aids. The first is a classic image mosaic, where the recorded and processed images are incrementally added, named 2D sequential image mosaicing (2DSIM). The second one geometrically transform the images so that they are projected as planar point clouds in the 3D space providing an incremental point cloud mosaicing, named 3D sequential image plane projection (3DSIP). In the paper, the implemented procedure is detailed, and experiments in different underwater scenarios presented and discussed. Technical considerations about computational efforts, frame rate capabilities and scalability to different and more compact architectures (i.e. embedded systems) is also provided.

Marine Robotics for Recurrent Morphological Investigations of Micro-Tidal Marine-Coastal Environments. A Point of View

“…Underwater mosaicking is a common topic in marine robotics. Relevant studies on this subject, which is fundamental to enhance the knowledge of underwater environments and a crucial tool for scientists, have been carried out by several researchers [63][64][65][66][67]. This can be performed exploiting optical or acoustic sensors.…”

Optimal Region Gradated Fusion Based Automatic Image Stitching for Industrial Systems

“…Author details 1 HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, China. 2 School of Compute Science and…”

“…Second, the transition area between the stitched images should be smooth and have the smallest difference in color intensity and geometric structure. The quality of image stitching technology [2][3][4] [5] directly determines the quality of the panorama. Image fusion is a key step of image stitching to eliminate stitching gaps, ghosting and other bad visual phenomenon [6].…”