# A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs
## 1 Introduction
Recently, the efficiency of incremental dictionaries has been improved by using binary features detected with ORB [7]. Moreover, other approaches [8] have shown that it is possible to use the Bayesian filtering technique without a dictionary. Instead, these store all of the features and index them within a kd-tree-based algorithm to match them efficiently. It is important to remark that the aforementioned approaches describe the images with regard to local features extracted with methods such as SIFT [9]. However, global image descriptors have also been used for place recognition. For example, in RatSLAM [10] a scan line intensity profile is used to globally describe images extracted from a suburb. A scan line is a one-dimensional vector formed by summing the intensity values in each column of the image. In [11], a patch-normalized reduced panoramic image of the surroundings is used directly as the descriptor. Despite the simplicity of the aforementioned descriptors, they both have been shown to perform well. However, for place recognition tasks, the global descriptors are more negatively affected if the images were captured from different points of view compared to describing scenes with local features. There are methods that combine both kinds of descriptors, for example in [12], where they use a Pyramid Histogram of Oriented Gradients (PHOG) [1] as a global descriptor to summarize neighboring images in the environment and local features detected with FAST [13] and described using a binary descriptor to find the similarities between the images. Recently, Convolutional Neural Networks (CNNs) have been used to generate global descriptor for images. For example, a pre-trained CNN, OverFeat [14], has been utilized to describe scenes and to match them to recognize places [15]. In [16], descriptors extracted from the CNN proposed in [17] are thoroughly evaluated for visual place recognition tasks. They found that the use of CNN-based descriptors can improve the recognition of places when there are changes in points of view and appearance. The previous approaches have focused on recognizing places by finding similarities between single images, however, other methods are based on matching sequences of images. The objective of matching sequences is to gain robustness against changes in the environment. For example, SeqSLAM [18] has been used to recognize places across different hours of the day or seasons of the year. More recently, in [19], they adapted SeqSLAM to include metric information and different filtering techniques to map outdoor environments. Inspired by SeqSLAM, other methods have been proposed that focus on reducing its time complexity. In [20], they proposed the use of a particle filter to avoid computing matching scores for all of the candidate sequences in the map. Other methods search among sequences defined by the most likely initial matching images [21]. An important drawback to notice is that those methods are designed to look for matching sequences in a given set of images obtained from a previous navigation in the environment. Despite the extraordinary results obtained by the aforementioned methods, their direct application to underwater environments is not always possible, mainly due to the inherent challenges of these places such as changes in illumination, color degradation, variable conditions in the environment due to sea currents, and low density of reliable visual features for tracking. Despite of that, in [22], the authors propose a method based on the use of an incremental BoVW to describe underwater imagery and generated good results. In [23], they used a Bayesian filtering technique similar to the one proposed in [8] to find loop closures in images obtained from explorations of coral reefs. There are other works that also perform mapping tasks in underwater environments using information from other sensors in addition to images.