## 2 Navigation and Localization
### Optical Navigation  
Optical technologies are a relevant option to provide information about the environment. These systems can be implemented either with a camera or with an array of optical sensors. Despite the poor transmission of light through water, which results in a limited range for imaging systems [68], different algorithms and techniques are being studied for this purpose. In Figure 11, two examples of optical systems are shown; where the AUV must detect and follow active landmarks within a structure (a) or identify a pattern made with active marks to navigate through it (b). Figure 10: Terrain-Referenced Navigation. An optical detector array sensor system was presented for AUV navigation by [PERSON] et al. [69]. The performance of the developed optical detector array was evaluated for its capability to estimate the position, orientation and forward velocity of AUVs regarding a light source fixed underwater. The results of computational simulations showed that a hemispherical frame design with a \(5\times 5\) photo-detector array was sufficient to generate the desired position and orientation feedback to the AUV with a detection accuracy of 0.2 m in translation (surge, sway, and heave) and \(10^{\circ}\) in orientation (pitch and yaw) based on a spectral angle mapper algorithm. Some of these optical or artificial vision systems have been applied to AUVs for different purposes such as docking and recovery. [PERSON] et al. [70] developed an artificial vision system capable of detecting a set of lamps located around the desired docking location for an AUV. The AUV uses a binocular localization method to locate the docking platform and navigates to reach it. Navigation lamps were mounted at the entrance of the docking station as active beacons. Three common underwater green lamps were symmetrically positioned on the docking model around the center of the three lamps. An experiment using a ship model has been conducted in a laboratory to evaluate the feasibility of the algorithm. The test results demonstrated that the average localization error is approximately 5 cm and the average relative location error is approximately 2% in the range of 3.6 m. A similar approach was proposed by [PERSON] et al. [71]. A vision-based framework for automatically recovering an AUV by another AUV in shallow water was presented in this work. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water, and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. At ground experiments, they observed that the mean position and orientation errors were 1.823\({}^{\circ}\) and 6.306 mm, respectively, in the absence of noise, and 2.770\({}^{\circ}\) and 9.818 mm, respectively, in the presence of strong noise. Field experiments were performed to recover a sub-AUV by a mother vessel in a lake using the proposed framework and experiments showed that the algorithm outperformed the state-of-the-art method in terms of localization error. Although these systems showed a response with high accuracy, pre-installed infrastructure is needed to implement them. An alternative approach is the use of a camera or set of cameras to identify features in the environment or targets for the AUV mission. [PERSON] et al. [72] developed a micro AUV with an artificial vision system that allows it to follow an object by its color. A Hue Saturation Value (HSV) filter was implemented on the artificial vision system and a non-linear proportional-derivative controller on the vehicle to stabilize the heave and surge movements. A search and recovery problem is addressed by an intervention AUV by [PERSON] et al. [73]. The problem consisted of finding and recovering a flight data recorder. The mission is compounded by two stages: survey and intervention. As the system was tested on a water tank, the survey stage consisted of a pre-defined trajectory of the AUV.