Regarding the topological maps, some related works propose arranging the information hierarchically, into several layers with different levels of granularity [3; 4].  
Hierarchical maps constitute a convenient framework to arrange the information. Notwithstanding, visual mapping remains an important research field in robotics due to the problems that the algorithms may have in heterogeneous and challenging environments. An important alternative to build hierarchical maps is by using some clustering techniques, such as spectral clustering [5]. Some researchers used spectral clustering methods along with visual information to build topological maps [6; 7; 8; 9]. Spectral clustering proved to be useful because it can cluster robustly highly dimensional information compared to other well-known methods [10; 11].  
Even though spectral clustering was used to build maps in mobile robotics with good results [11], the problems of using the standard spectral method in incremental mapping are threefold. Firstly, most spectral clustering methods require the number of final nodes to be indicated previously. Secondly, computing similarities among entities can be hard in terms of computational cost. This is especially noticeable when the data set becomes large. Finally, as a consequence of the previous problem, it is not possible to perform spectral clustering _on-line_ when the dataset is constantly growing (i.e., The robot is continuously moving and capturing new images which must be included in the model).  
Presently, incremental mapping is a key ability because it would enable mobile robots to gradually build or update a model as they explore the initially unknown environment and capture new information. For this purpose, incremental clustering methods can be useful [12].  
In this work, we propose a framework based on incremental clustering with the objective of creating a topological hierarchical map incrementally, using only visual information. Each group of similar images will be included in a cluster with a representative descriptor associated to it. To avoid the necessity of setting the number of clusters beforehand, our proposal defines a set of adaptive thresholds. Depending on them, the algorithm behaves more or less restrictively and therefore creates a higher or lower number of clusters as it progresses. In addition, the method proposed in this paper is able to perform _on-line_, updating the model every time the robot captures a new image.  
When using computer vision to build a model of the environment, it is important to verify the performance of the method when the visual appearance of the environment changes substantially. In real-operating conditions the robot has to cope with different events: different lighting conditions during the operation, scenes partially occluded by people or other mobile robots and changes in the scene due, for example to the furniture position. For that reason experiments were carried out in a real environment during working hours.  
The remainder of the paper is structured as follows. In the following section, related works on visual information and incremental and hierarchical mapping are outlined. In Section 3, global appearance descriptors are defined. The mapping method is presented in Section 4. In Section 5, relevant information about the datasets, tools and acquisition equipment used in the experiments is presented. In Section 6, the results of the experiments are shown. Finally, conclusions and future research lines are presented in Section 7. An extra section named 'Supplementary Materials' includes some tables of symbols, which summarize all the variables and parameters used to describe the proposed method.