## 2 Navigation and Localization
### Simultaneous Location And Mapping (SLAM)  
Simultaneous Location and Mapping (SLAM) is a technique that consists of a mobile robot, such as an AUV, being placed at an unknown location in an unknown environment and make it able to build a consistent map of the environment and determinate its location within this map [74]. In Figure 12, a SLAM solution is represented where an AUV is equipped with a sensor to explore the environment to create a digital reconstruction of it. Color codes can be used to represent information such as distance between the vehicle and obstacles. There are different SLAM representation methods used to reconstruct the environment. Each one has its own shortcomings and advantages, choosing the best one depends on the application desired which can be inspection, navigation, interaction, etc. The principal representation methods are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Type** & **Description** & **Applications** \\ \hline \multirow{3}{*}{landmark-based maps} & \multirow{3}{*}{2D/3D} & Models the environment as a set of landmarks extracted from features as points, lines, corners, etc. & Localization and mapping [75]. \\ \hline \multirow{3}{*}{Occupancy grid maps} & \multirow{3}{*}{2D} & Discretizes the environment in cells and assigns a probability of occupancy of each cell. & Exploring and mapping [76]. \\ \cline{1-1} \cline{2-3}  & & Describes the 3-D geometry by a large unstructured set of points or polygons. & Obstacle avoidance and visualization [77]. \\ \hline \multirow{3}{*}{Boundary and Spatial-Partitioning Dense Representations} & \multirow{3}{*}{3D} & Generates representations of boundaries, surfaces, and volumes. & Obstacle avoidance and manipulation [78]. \\ \cline{1-1} \cline{2-3}  & & Describes the 3-D geometry by a large unstructured set of points or polygons. & Obstacle avoidance and visualization [77]. \\ \hline \multirow{3}{*}{Boundary and Spatial-Partitioning Dense Representations} & \multirow{3}{*}{3D} & Generates representations of boundaries, surfaces, and volumes. & Obstacle avoidance and manipulation [78]. \\ \cline{1-1} \cline{2-3}  & & Describes the 3-D geometry by a large unstructured set of points or polygons. & Obstacle avoidance and visualization [77]. \\ \hline \multirow{3}{*}{Boundary and Spatial-Partitioning Dense Representations} & \multirow{3}{*}{3D} & Generates representations of boundaries, surfaces, and volumes. & Obstacle avoidance and manipulation [78]. \\ \cline{1-1} \cline{2-3}  & & Describes the 3-D geometry by a large unstructured set of points or polygons. & Obstacle avoidance and visualization [77]. \\ \hline \multirow{3}{*}{Boundary Spatial-Partitioning Dense Representations} & \multirow{3}{*}{3D} & Generates representations of boundaries, surfaces, and volumes. & Obstacle avoidance and manipulation [78]. \\ \cline{1-1} \cline{2-3}  & & Describes the 3-D geometry by a large unstructured set of points or polygons. & Obstacle avoidance and visualization [77]. \\ \hline \multirow{3}{*}{Boundary Spatial-Partitioning Dense Representations} & \multirow{3}{*}{3D} & Generates representations of boundaries, surfaces, and volumes. & Obstacle avoidance and manipulation [78]. \\ \cline{1-1} \cline{2-3}  & & Describes the 3-D geometry by a large unstructured set of points or polygons. & Obstacle avoidance and visualization [77]. \\ \hline \multirow{3}{*}{Boundary Spatial-Partitioning Dense Representations} & \multirow{3}{*}{3D} & Generates representations of boundaries, surfaces, and boundaries, surfaces, and volumes. & Obstacle avoidance and manipulation [78]. \\ \cline{1-1  
Underwater SLAM can be categorized in acoustic-based and vision-based [38]. The perception of optical devices is constrained by poor visibility and noise produced by sunlight in shallow waters. Moreover, they can provide high frequencies and high resolution for a lower cost than an acoustic system. On the other hand, a high-definition FLS can provide a promising alternative for working under challenging conditions. In [79], [PERSON] et al.