A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs

One of the most important tasks when creating a map of visual information obtained from different agents is finding common locations between the sets of images that enable them to be fused into a single representation. Typical approaches focus on images obtained from the same agent. However, in this paper, we focus on recognizing the same places in images captured by different agents to create a topological map of coral reefs. The main components of the proposed method are the voting scheme to find a sparse similarity matrix between different frames and an effective method to match sequences of images exploiting the sparsity of the resulting similarity matrix. We have applied our method to sequences of images obtained from coral reef explorations performed by different agents. The presented method shows a good performance compared to other well-established methods such as FABMAP. This demonstrates its ability to find common locations from visual information gathered from different sources, which eases the collaboration between humans and robots to map the environment.