# A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs
## 1 Introduction  
Although robots are widely used in underwater environments to create maps (topological maps or seafloor mosaics), these are typically created by a single robotic agent without interaction or support from other agents. Adding help from other agents can benefit the created map by enriching with more information from cameras with better resolution or even with information from other points of view. In addition to the use of other robotic agents, it can be helpful to include information captured by humans into the mapping process. A diver could provide images from parts of the coral reef that are of interest to other researchers. For example, while a robot can be programmed to follow a zig-zag trajectory above a coral reef, a human can navigate the same environment closer to certain species of coral, thereby providing detailed images from the areas of interest. However, creating a map of information from more than one agent is not trivial, specifically because it is necessary to recognize the same places in images taken with different cameras, under different environmental conditions, and with different points of view. Moreover, the information captured by a diver can be more challenging to handle with regard to point of view variations because it is more difficult for human explorers in these environments to maintain a constant orientation and distance with respect to the seafloor compared to robotic agents. Therefore, to recognize places in different images (loop closure detection),whether they are captured by the same agent or not, one of the most relevant challenges to tackle is generating a robust image description that allows the identification of two images taken from the same place despite changes in appearance and point of view. It also should be distinctive enough to discard two similar images from different places. With respect to the image description, there are two main approaches: using a global descriptor for the whole image (e.g., Pyramid Histogram of Oriented Gradients [1]) or describing an image with respect to the contained local features (e.g., Speed-Up Robust Features [2]). However, combinations of both approaches can also be applied. One of the most known methods for visual place recognition is FAB-MAP [3], where local features are used to generate a Bag of Visual Words (BoVW) [4] to describe images with respect to the frequency of occurrence of features contained in a codebook or dictionary. This method uses a Chow-Liu tree to approximate the dependency between the occurrence of the detected visual features. They have obtained very good results for outdoor environments despite the existence of perceptual aliasing (i.e., images that are perceptually similar but from different scenes). A dictionary or codebook for this kind of method is a collection of representative visual features that can be found in the environment of interest and it is typically built by clustering similar local features extracted from a sample of images of the environment. The use of a dictionary helps to improve the efficiency of the method, as an image can be described only in terms of the presence of the features contained in the dictionary. The overall performance of a Bag of Visual Words method depends on having good features in the codebook. It is important to mention that there are also approaches [5; 6] that incrementally create a dictionary, i.e., they cluster similar features into visual words as they are extracted. If a feature is not similar to any of the existing features in the codebook, it is added as a new visual word. In [5], instead of describing an image directly with regard to the frequency of occurrence of visual words, they focus on registering in which images each word from the vocabulary appears; this enables comparison between two images with respect to which visual words they share. Additionally, they used a Bayesian filtering technique to recognize previously seen places.