# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 6 Conclusions and Future Works  
This paper presents a study about the use of CNNs to carry out the hierarchical localization by means of omnidirectional images in indoor environments. A classification CNN is trained to address the room retrieval by using a transfer learning technique. Additionally, transfer learning is used again to transform the CNN in a regression CNN and thus addressing the pose estimation within a specific room. The architecture of the network used produces acceptable results regarding accuracy and training time. The results obtained for the room retrieval task are considerably successful, since the percentage of  
Figure 9: Clusters obtained with spectral clustering for (**a**) corridor with 5 divisions and (**b**) office with 2 divisions.  
success is 98.11% and the majority of the few confusions are given in the frontiers between different rooms.  
As for the regression CNN, the initial results cannot be considered accurate, since considerable errors arise in some specific rooms, such as the corridor. Nonetheless, after applying an improvement strategy based on splitting in areas those rooms in which the robot runs a long, linear trajectory, the new networks are able to output more accurate results.  
Concerning the improvement of the regression CNNs, several alternatives could be considered in future works to continue improving the results. On the one hand, splitting the rooms into smaller areas (subrooms) and generating more training images by a data augmentation technique, in such a way that the networks are more robustly trained to estimate the position of the robot in each room. On the other hand, the pose estimation could be addressed by using recurrent networks instead of regression networks. In this way, the current position of the robot within the room would be estimated considering the previously estimated poses. Additionally, using more recent CNN architectures could permit extracting more robust features and then the regression network could provide better results.  
Finally, other future research lines include the performance of the proposed method with other datasets whose environments present different challenges, such as outdoor environments or different capturing strategies. Moreover, a hierarchical localization approach based on Long Short Term Memory (LSTM) networks will be developed.  
Conceptualization, M.B. and L.P.; methodology, L.P.; software, S.C. and F.M.; validation, S.C., M.B. and O.R.; formal analysis, O.R. and L.P.; investigation, S.C. and M.B.; resources, M.B. and L.P.; data curation, L.P.; writing--original draft preparation, S.C. and M.B.; writing--review and editing, M.B., L.P. and O.R.; visualization, F.M.; supervision, M.B.; project administration, M.B. and O.R.; funding acquisition, M.B. and O.R. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Spanish government through the project PID2020-116418 RB-I00: \"HYREBOT: Robots hibridos y reconstrucion multisensorial para aplicaciones en estructuras reticulares\".  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.cas.kth.se/COLD/cold-freiburg.html](https://www.cas.kth.se/COLD/cold-freiburg.html)]([https://www.cas.kth.se/COLD/cold-freiburg.html](https://www.cas.kth.se/COLD/cold-freiburg.html)) (accessed on 6 August 2021).  
The authors declare no conflict of interest.