# A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs
## 5 Conclusions  
In this paper, a novel method is proposed for creating topological maps of underwater environments using visual information provided by different exploring agents. As the visual information that is utilized was captured under different conditions and by different agents, a robust method to recognize previously seen places is needed. Without a robust approach, the collaboration between different agents to create a map would be more difficult. Our solution centers on sequence-based methods since they have shown good results in challenging environments with strong changes in appearance. Moreover, our method deals with images incrementally, which allows it to create a map online. Toward this end, we calculate the similarity matrix required in sequence-based methods, but in an incremental manner. A voting scheme combined with local visual features is utilized to generate a similarity vector (containing the similarity between the current image and the previous ones) that is added as the last row in the current similarity matrix. The calculated matrix is sparse enough to only look for sequences of matching images at certain points. In addition to the topological information between the images, the map also incorporates an approximation of the spatial structure by concatenating the relative positions between images. To maintain consistency in spatial structure, every time a place from previous images is recognized, the positions in the graphs are adjusted. Finally, to reduce the amount of repeated information that is added to the map we used a sampling method that only adds consecutive images with maximum intersecting areas.  
The proposed method has been evaluated with regard to the Precision-Recall ability to recognize previously visited places when using images captured by different agents and when varying the keyframe selection criteria. As presented in the results, the proposed approach manages those factors by striking a compromise between the Precision-Recall and the number of keyframes added to the map. After, we compared the spatial structure of the map obtained after reducing the number of keyframes. We observed that there is no significant difference between the spatial structures when our method uses fewer images than those contained in the original dataset.  
In terms of future work, we are interested in testing other types of local features, distinctive enough to require only a few of them, thereby, improving the scalability of the proposed approach. In particular, we are interested in testing the use of features obtained using deep convolutional neural networks as these have shown promising results in matching visual patterns.  
**Author Contributions:** This paper has two authors. Both authors made most of contributions: conceptualization, development of theory, validation, verification of the analytical methods, sea trials, formal analysis and investigation, discussion of results and contributed to the final manuscript. Individual contributions follows: performed the computations and software, data curation, visualization, writing original draft preparation, A.A.M.R.; writing review and editing, resources, supervision, project administration, funding acquisition, L.A.T.M.  
**Funding:** This research was funded by CONACyT Mexico, grant number CB-2013-220540.  
**Acknowledgments:** We thank Mar Adentro Diving, Mahahual, for their support during our sea trials.  
**Conflicts of Interest:** The authors declare no conflict of interest.