Algorithmically Improved Framework for Image-only Robotic Mapping

Over the past two decades, technological developments in the robotics field have made it possible to gather data from areas that are hostile and not-reachable environments for humans. In line with these advancements in data collecting tools and procedures, the need and demand for computationally efficient methods for processing the gathered data have been increased from different science and engineering fields. Among the others, optical data is one of the main data sources that low-cost robotic vehicles can obtain easily nowadays. Due to the different limitations, obtained optical data usually cannot cover a large area in a single image. Therefore, optical mapping methods (image mosaicing) are needed to create higher resolution maps by combining comparatively smaller resolution images. These methods rely on pairwise image registration and one of the main bottlenecks in the case of image-only information available is that the quadratic growth of image matching attempts with respect to the total number of images. In this paper, we propose an algorithmically improved end-to-end framework for creating 2D optical maps from a set of randomly ordered images with the aim of reducing computational efforts needed via lowering the total number of image matching attempts. We present extensive and comparative experimental results with its counterpart approach using four real datasets obtained from the underwater environment using Unmanned Underwater Vehicles (UUVs).