# A Collaborative Human-Robot Framework for Visual Topological Mapping of Coral Reefs
## 3 Results
### Loop Closure Detection  
In this section, we present the ability of our method to recognize previously seen places in terms of Precision-Recall curves for the aforementioned datasets. For comparison purposes, we have included a Bayesian filter-based method to recognize places [8]. Also, we included FAB-MAP 2.0 [39] as it is one of the most representative methods based on the Bag-of-Words paradigm. For FAB-MAP, we used the open implementation described in [43]. Finally, SeqSLAM [18] has also been incorporated into the comparison as it is an important piece of work among the sequence-based methods for detecting loop closures. For SeqSLAM, we utilized its open implementation (OpenSeqSLAM: [[http://nikosuenderhauf.info/code](http://nikosuenderhauf.info/code)]([http://nikosuenderhauf.info/code](http://nikosuenderhauf.info/code))). From the ground truth of a dataset and the pair of images representing the same place detected by the evaluated method, the Precision-Recall curve can be calculated. This curve relates the precision and recall values when varying a threshold for accepting a pair of images as loop closure, i.e., a pair of images taken from the same place. The precision is the ratio between the retrieved loop closures that appear in the ground truth and all the loop closures detected by the evaluated method. The recall indicates the ratio between the retrieved true loop closures by the method and all the possible loop closures according to the ground truth. The desirable curve for a method is the one that reaches the highest possible recall with a precision of 100%. This means that the method is very likely to recognize the same place without confusing the location. This is important because a single false positive detection can cause spatial and topological inconsistencies in the representation of the environment. To get the ground truth pairs of corresponding images, we performed a direct comparison of the visual features between all of the images included in a dataset. Then, we check that every pair of images truly corresponded to the same place before adding it to the ground truth. For our proposed approach, the Bayesian one presented in [8] and FAB-MAP, SURF [2] is used as feature detector and descriptor. In the case of FAB-MAP, we built a new vocabulary from a collection of underwater images captured during previous explorations. For FAB-MAP, its Precision-Recall curve is computed by varying the threshold related to the minimum probability for loop closure acceptance. As for SeqSLAM, the trajectory uniqueness threshold has been varied. In the Bayesian filter-based approach we have varied the probability threshold related to the recognition of previously visited places. In our approach, we varied the minimum threshold \(\lambda\) for accepting two sequences of images as taken from the same place. In Table 2, we present the rest of the parameters for our approach. It is important to note that we have tested the aforementioned methods using three different maximum overlap values \(ol_{max}=\{1.0,0.5,0.25\}\) to select keyframes. The smaller \(ol_{max}\) is, the fewer images are used to build the topological map; thus, increasing the difficulty to recognize previously seen places. When all the images from each dataset are utilized (\(ol_{max}=1.0\)), as seen in Figure 9, the proposed approach exhibits a good performance in terms of precision and recall, i.e., it is capable of obtaining 100% precision with a good recall value. The more challenging cases can be observed in Figures 10 and 11 when \(ol_{max}=0.5\) and \(ol_{max}=0.25\) respectively. This means that only consecutive images with at most a 50% and 25% overlap are added to the topological map. This complicates the visual place recognition since it is more likely that images from the same place share fewer visual