# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 6 Conclusions
Once this position and orientation is known, the methods exposed in the present paper could be used to estimate topologically the relative height \(z\). Figure 21: Results of the experiments in conditions (**c3**) using outdoor (blue continuous curves) and indoor (dashed curves) sets of images and considering all the combinations _method-image projection-descriptor_ shown in Table 3. Only negative vertical height gaps are considered in this experiment. (**a**) Method 1 + Pano. Im. + FS. (**b**) Method 1 + Pano. Im. + 2D-DFT. (**c**) Method 2 + Pano. Im. + 2D-DFT. (**d**) Method 3 + Orthogr. View + FS. (**e**) Method 3 + Orthogr. View + 2D-DFT. (**f**) Method 4 + Pano. Im. + FS. (**g**) Method 4 + Pano. Im. + 2D-DFT. (**h**) Method 4 + Orthogr. View + FS. (**i**) Method 4 + Orthogr. View + 2D-DFT. (**j**) Method 4 + Unit Sphere Proj. + SFT. (**k**) Method 5 + SURF. (**l**) Method 5 + SURF + RANSAC. The methods based on the orthographic projection, both using the Multiscale Analysis and the CRS Movement, have presented the best performance. It is interesting to highlight that, despite the fact that the height estimators calculated cannot be considered metric estimators, they go beyond the classical concept of topological distance because they contain not only connection or neighborhood relations but they also give us an idea of closeness or farness from the reference image. This is an interesting conclusion as it would permit using these estimators to build hybrid maps of a large environment, including height information, using the methods explained in [34]. Future works will focus on this mapping line, since having complete maps of an environment would be very useful in many applications, when the robot has to estimate this position with more than 3 DOF. Also, these methods may be complemented to estimate the pose of the mobile platform when it has 6 DOF, using a unique initial model that contains information on all the necessary DOF. For this purpose, the SFT seems to be the most suitable descriptor, thanks to its invariance against rotations around any axis, and a deeper experimentation with this method could be carried out. This work has been supported by the Spanish Government through the project DPI 2016-78361-R (AEI/FEDER, UE): \"Creacion de mapas mediante metodos de apariencia visual para la navegacion de robots\". This project has also provided funds for covering the costs to publish in open access. The work presented in this paper is a collaborative development by all the authors. [PERSON], [PERSON] and [PERSON] defined the research line. [PERSON], [PERSON] and [PERSON] acquired the database. [PERSON], [PERSON], [PERSON] and [PERSON] designed and implemented height estimation algorithms and performed the experiments. Finally the authors wrote collaboratively the document. The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DOF & Degree of Freedom \\ WRS & World Reference System \\ CRS & Camera Reference System \\ IRS & Image Reference System \\ DFT & Discrete Fourier Transform \\
2D-DFT & Two-Dimensional Discrete Fourier Transform \\ FS & Fourier Signature \\ SFT & Spherical Fourier Transform \\ VRM & Vertical Rotation Matrix \\ \end{tabular}