## 7 Conclusions  
This work presented a method to create hierarchical topological maps incrementally, updating the map every time a new image arrives. The framework is based on the development of an incremental clustering algorithm, presented throughout the paper. The experiments were made in real indoor  
Figure 15: Maps obtained during the process using HOG \(y=0.75\), \(\Omega=1.7\) with the Freiburg route. The subfigures show different steps of the process. In the end, the process detects 30 nodes and the final average silhouette is \(-0.1526\). The pair of **e,f** shows how the node merging process works. environments where the robot navigates under real operation conditions, including illumination variations and changes introduced by human activity. The robot is equipped with an omnidirectional vision system, and the only information used to build the hierarchical map are the images captured by this system. To describe the images two different global-appearance methods were considered: HOG and gist. The experimental section showed the performance of the proposed algorithm and the effect of the most relevant parameters on the final result. Also, a comparative evaluation with a batch spectral clustering algorithm is performed. The relative accuracy of the method is studied by means of the average silhouette, calculated considering as entities the capture points of every image (ground truth). First, HOG proved to output better results, such that the average silhouette obtained with the proposed incremental method is similar to the silhouette output by the batch spectral clustering method despite the fact that the proposed algorithm only has partial information at each step. In fact, if the parameters are tuned properly if can offer better results than the batch spectral clustering. The results show that in the case of HOG, it is especially important tuning correctly the parameter \(y\), as it has a strong influence on the final number of clusters. Also, the performance of the proposed algorithm degrades when the dataset contains an excessively high number of images, presents complex features (i.e., numerous windows or glass walls) or is prone to visual aliasing. The results might be considered successful since our incremental algorithm starts with a reduced number of images and updates the map (clusters) every time a new image arrives whereas the batch clustering algorithm has complete information on all the descriptors from the beginning of the process, and can calculate all the mutual similitudes to perform the clustering process. Additionally, gist proved to perform less robustly and the silhouettes obtained with batch spectral clustering are relatively higher. Therefore, the proposed incremental method along with omnidirectional images and the HOG descriptor constitutes the most suitable option to perform incremental mapping. This work opens the door to new research works on incremental hierarchical map creation using global-appearance description methods in mobile robotics. Once we have tested the robustness and efficiency of the methods in real environments including human activity and presence of changes in the position of some objects, the next step is to improve the algorithm and adapt it to be used in large and outdoor environments. In this sense, we will focus in particular on the problem of abrupt changes of the lighting conditions and changes across seasons, as it is one of the issues that may have a more negative impact upon the visual mapping algorithms. The following are available online at [[http://www.mdpi.com/2076-3417/10/18/6480/s1](http://www.mdpi.com/2076-3417/10/18/6480/s1)]([http://www.mdpi.com/2076-3417/10/18/6480/s1](http://www.mdpi.com/2076-3417/10/18/6480/s1)), Table S1: General parameters of the global appearance descriptors, Table S2: Parameters that impact the size of the image descriptors; Table S3: Symbols used in the hierarchical incremental mapping processes, Table S4: Parameters that need to be tuned, Figure S1.