# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) represents an essential task to be dealt with in mobile robotics. This paradigm comprises a simultaneous estimation of the map and the localization of the robot. This fact implies a real challenge when it comes to complexity, due to the incremental nature of this process, which is very sensitive to the non-linear effects that normally affect the systems in the form of added noise to the input or just in terms of coupled external noise. Both definitely compromise the convergence of the system estimation gravely [1; 2]. Under this context, different research lines have been conducted in the field of mobile robotics. It is worth highlighting the evolution of the sensors to acquire information from the environment. Originally, laser [3; 4; 5] and sonar [6; 7] were widely acknowledged. Despite the fact that these sensors are still strongly accepted nowadays [8; 9; 10], they are being slightly relegated as secondary sensory data to the detriment of visual sensors, which have been experienced an growing use recently. Digital cameras have had a great success, being one of the most feasible tools for acquiring information from the environment. They represent a promising alternative due to their low cost, lightness, low consumption, precision and efficiency to process visual data. They also become an encouraging option for complementing other sensors. In this sense, recent research lines rely on a more extensivecombination of visual data with multiple light-computation sensory data, such as IMU (Inertial Measurement Unit) [11], RGB-D [12; 13] and GPS [14], which are especially oriented to the wide diversity of UAV (Unmanned Aerial Vehicle) applications that are deployed these days [15; 16; 17]. Amongst other visual sensors, omnidirectional cameras are remarkable for their capability to acquire a wide field of view with a unique snapshot. Different approaches have focused on computer vision techniques by using single cameras with visual encoding of 3D landmarks [18; 19], stereo-structure [20; 21; 22] and omnidirectional cameras [23; 24; 25; 26]. In particular, many approaches support matching systems that take advantage of the image side [27; 28], but also the mapping side [29]. In this paper, we also rely on the potentials of the matching in order to associate visual information. This association represents the basis for an advanced feature matching, in contrast to generally known approaches [30; 31]. Consequently, we are able to extend the localization developments to a wider field of application: visual SLAM. Such context implies that huge efforts have to be addressed in order to assure convergence for the estimation and uncertainty control [1; 32]. The core of the approach is sustained by a previous work, in which a view-based monocular EKF was devised [33] under a design that focused on a compact view-map with a reduced set of omnidirectional frames, which are mutually seen as image and landmark at once. This allows us to re-estimate images and also the entire map at a single step for each \(t\), being then more efficient in contrast to large 3D-landmark maps' re-estimation [19; 34]. Besides this, neither parametrization nor tracking are required. Its performance was evaluated [32; 35] within a comparison framework under the cover of the original fundamentals for such a SLAM basis [36; 37; 38], which established the initial foundations for the improved implementations and contributions of the present paper. Nonetheless, the aim of this approach pursues the specific improvement of the localization by considering the mentioned casuistry in terms of convergence and uncertainty control, rather than further developments or analysis of the SLAM core.