# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 7 Conclusions  
This article has presented a robust visual localization technique for mobile robotics applications, achieved with a monocular omnidirectional sensor system, constituted by a hyperbolic mirror and a digital camera. Several contributions have been designed and implemented in order to deal with inconsistencies and non-linearities, which especially affect visual sensors. A dynamic uncertainty management control has been devised so as to adaptively conform to the instant variations on the uncertainty of the system. These variations are principally induced by the noise input associated with the changing scene conditions. Moreover, Bayesian regression has also been introduced in order to take most of the sensor data distribution, which ultimately is used to define an informative metric that permits designing robust weighting coefficients for the visual matching block. The adaption of the epipolar geometry towards the omnidirectional nature of the omnidirectional sensor system was also implemented.  
The contributions presented herein have been tested in terms of validity and reliability. The results obtained with real datasets have proven the suitability of this realistic robust localization proposal. A tradeoff configuration can be selected in order to operate in real time with sufficient accuracy, considering the specific requirements of the visual SLAM equipment. Besides this, the utilization of visual data has also been improved, since more matching points are detected, in contrast with standard approaches, which tend to fail to gather trustworthy information. Finally, the ultimate SLAM results have confirmed the robustness of the localization proposal when it is embedded in such a real visual system. The final estimations demonstrate consistency in keeping stability and convergence in the system, besides a good tradeoff balance between accuracy and performance. Moreover, the additional comparison results reinforce the validity of this approach.  
This work has been partially supported by the Spanish government through the project DPI2016-78361-R (AEI/FEDER, UE). In addition, part of the developments has been achieved during a post-doctoral grant for a research stay at the Technical University of Madrid, under a collaboration framework established by the post-doctoral grant APOSTD/2017/028, held by [PERSON], funded by the Valencian Education Council and the European Social Fund.  
This work emerged under a collaboration framework established between all the authors. [PERSON], [PERSON] and [PERSON] conceived of the target and research line. [PERSON], [PERSON] and [PERSON] defined the software programming planning. [PERSON] implemented the main software contributions and algorithms. [PERSON] and [PERSON] acquired the real dataset and established the experimental benchmark. [PERSON] performed the experimental tests. [PERSON] conducted the data processing, and [PERSON] collaborated in the data analysis within the experimental section.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SLAM & Simultaneous Localization and Mapping \\ IMU & Inertial Measurement Unit \\ UAV & Unmanned Aerial Vehicle \\ FEM & Finite Element Method \\ CCD & Charge Coupled Device \\ KL & Kullback-Leibler divergence \\ EKF & Extended Kalman Filter \\ SVD & Single Value Decomposition \\ RMSE & Root Mean Square Error \\ \end{tabular}