# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 4 Robust Localization within Omnidirectional SLAM  
Visual SLAM symbolizes a widely-acknowledged final navigation application in mobile robotics. According to this, we fused the proposed robust localization into an omnidirectional SLAM system [33]. Hence, this section condenses the fundamentals of such a system [56], under the cover of the essentials of a verified EKF-SLAM method [19; 36], with the proposed robust localization now embedded.  
This omnidirectional SLAM approach entails an improved view-based map model with respect to former works, as may be noted in Figure 10. The estimated map is composed of a reduced set of omnidirectional images, or views, \(x_{n}=(x,y,\theta)_{n}^{T}\), acquired from different poses along the traversed path. Each \(n\)-view encodes visual information in terms of SURF feature points [57]. The pose of the  
Figure 8: Evolution of the sensor data information distribution along poses, expressed in terms of the probability of existence, for example in Figure 6. (**a**): pose \(A\); (**b**) pose \(B\); (**c**) pose \(C\).  
Figure 9: Complete workflow for the system operation, as detailed in Section 3.3.  
robot at \(t\) is denoted as \(x_{r}=(x_{t},y_{t},\theta_{t})^{T}\). Thus, the definition for a state vector considers \(x_{r}\) and \(x_{n}\), with the following 2D structure:  
\[x_{v}(t)=\begin{bmatrix}x_{r}&x_{1}&\cdots&x_{n}&\cdots&x_{N}\end{bmatrix}^{T} \tag{14}\]  
each view being \(n\in[1,\ldots,N]\). Then, the state vector encodes a map constituted by a total number of \(N\) views. It is also worth noticing that each \(x_{n}\) accounts for the visual encoding of a specific area of the environment, so that the robot can always localize itself.  
It is essential to notice the duality characteristic of this mapping approach. It presents a capability of producing both 2D and 3D results, despite the fact that the robot has a monocular camera mounted, and thus, the images are accordingly acquired in a 2D reference. Certainly, the visual information is compressed on the 2D image frame by feature points. However, they express the same information as 3D landmark-based approaches [36; 58]. As explained in Section 3, by using a motion prediction together with uncertainty propagation, the lack of scale may be retrieved, and so 3D information. As a result, and contrary to previous 3D landmark models, the re-estimation of the 3D pose of every landmark in the environment is no longer needed. Now, with this approach, a single re-estimation of a view (position), as part of \(x_{v}(t)\), inherently implies the whole re-estimation of the visual feature points inside it and, at the same time, the re-estimation of the complete map itself, since it is composed by views as a whole. This is a simpler procedure, which allows one to re-estimate at once all the views with all the visual information contained, represented by the set of feature points encoded at each view. Finally, it produces a single and complete map re-estimation. In order to get a clearer picture of this omnidirectional SLAM structure, its workflow may be divided into: (i) view initialization in the map; (ii) observation measurement; (iii) data association. Dealing with localization tasks leads us to concentrate on Stage (ii), which is consequently presented in the following subsection.  
Figure 10: Dual 2D-3D map model composed by omnidirectional views, \(x_{n}\). Information is encoded on the 2D image plane by feature points over each view, being able to be recovered on the 3D reference by estimating a scale factor. The re-estimation of \(x_{n}\) implies the whole re-estimation of the map at once, contrary to former 3D-landmark maps.