## 4 Avinomics Assessment
### Artificial Intelligence (AI)-based techniques
#### Localization  
The localization of a UAV is the process of determining its position in a given environment, involving the obtainment of accurate information about the UAV's spatial coordinates (i.e., latitude, longitude, orientation, etc.). In relation to other navigation hierarchies (e.g., path planning), it enables a precise execution of the planned paths and strategies [141; 142; 143; 144; 145]. Concerning vision-based UAV localization techniques, they can be characterized according to two main sets as follows: Relative Visual Localization (RVL) [146; 147] and Absolute Visual Localization (AVL) [148]. Among RVL methods, the popular Visual Odometry (VO)-based techniques [149] analyze the difference in \"ego-motion\" by comparing between current and previous frames while performing with Optical Flow (OF) analysis [150]. Generally, all RVL methods suffer from drift over time [151], ignited by the usage of recursive estimations in order to formulate new estimations. Because of this, AVL methods have evolved in the field of UAV localization, mainly because of their inherent immunity against drift over time. Unlike the working mechanisms for UAV localization in RVL methods, AVL localizes a UAV by means of reference data, which are mainly composed of precisely georeferenced aerial images [152]. For example, a UAV's absolute position can be acquired by means of normalized cross-correlation [153], as well as via a Mutual Information (MI)-based dense approach, as shown in Figure 2[154].  
The global reference map in Figure 2 is constructed from a mosaic of georeferenced images, such that an \"sRt\" model (i.e., accounts for the 3D motions) is produced for usage  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Algorithms Set** & \multicolumn{2}{c}{**Working Mechanism**} \\ \hline \multirow{4}{*}{Sample-based} & \(\bullet\) & Divide the environment into a set of nodes. \\  & \(\bullet\) & Connect nodes via “depth-first” search procedure. \\  & \(\bullet\) & Begin the searching process for the optimal UAV route. \\  & \(\bullet\) & Easy to implement and better work online. \\ \hline \multirow{4}{*}{Mathematical-based} & \(\bullet\) & Describe constraints (i.e., dynamic/kinematic) mathematically. \\  & \(\bullet\) & Bind the cost function. \\  & \(\bullet\) & Require high computational resources. \\  & \(\bullet\) & Work better offline. \\ \hline \multirow{4}{*}{Multi-fusion} & \(\bullet\) & Integrate several algorithms. \\  & \(\bullet\) & Save time. \\  & \(\bullet\) & Appropriate to work online. \\ \hline \multirow{4}{*}{Bio-inspired} & \(\bullet\) & Heuristic-based. \\  & \(\bullet\) & Excellent handling of complex unstructured constraints. \\ \cline{1-1}  & \(\bullet\) & Mutation is a key factor for path optimization. \\ \cline{1-1}  & \(\bullet\) & Appropriate to work offline. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Assessment of different sets of path planning and obstacle avoidance algorithms.  
Figure 2: UAV localization through a set of georeferenced images [154].  
after an assumption of a planar ground and a UAV image that is parallel to it, defined as in (1).  
\[w(X_{t},\mu)=sR_{2d}x_{t}+t_{2d} \tag{1}\]  
where \(R_{2d}\) represents a 2D rotation, \(s\) is a scale factor, \(x_{t}\) is a point in space, and \(t_{2d}\) is a 2D translation. The MI function's maximum is found using [PERSON]'s optimization warped in the \"sRT\" model with respect to the global reference map. Results indicate small relative mean squared errors in the localization data [154]. AVL methods for UAV localization overcome the drawbacks of GNSS-based localization methods, such as reception issues [155], spoofing attacks [156], and signal degradation [157].