## 2 Navigation and Localization
### Simultaneous Location And Mapping (SLAM)
presented a framework to give an AUV the capability to explore unknown environments and create a 3D map simultaneously with an acoustic system. The framework comprises two main functional pipelines. The first, provides the AUV with the capacity of creating an acoustic map online, while planning collision-free paths. The second pipeline builds a photo-realistic 3D model using the gathered image data. This framework was tested in several sea missions where results validated its capabilities. [PERSON] et al. [80] used a multi-beam echo-sounder to produce high consistency underwater maps. Since there is not a general method to evaluate consistency of a map, authors computed the consistency-based error [81] and proposed a 3D statistic method named #Cells. The statistic method consisted in counting the number of cells that each bathymetric map occupies within the same 3D grid. If a map occupies less cells, it is probably because their point clouds are more densely packed due to a better registration. The algorithm was tested using two real world datasets. Three surfaces were created for different navigation methods: DR, USBL and the proposed algorithm. Regarding the number of occupied cells, the proposed method occupied 5.76% less cells than a DR model, and 7.24% less than the USBL model. [PERSON] et al. [82] implemented a visual-based SLAM algorithm. Authors compared a stereo Point and Line SLAM (PL-SLAM) with an Orientated FAST and Rotated BRIEF (ORB) SLAM, a point-only system and a line-only system. Results showed superior performance of the PL-SLAM approach relatively to ORB-SLAM, in terms of both accuracy and robustness in most of the dataset sequences. The mean translational error was minor for PL-SLAM in 55% of the sequences and the mean rotational error in the 73% of the cases. Nevertheless, that work was not tested for underwater applications. After that, [PERSON] et al. [83] proposed a method to improve the accuracy of vision-based localization systems in feature-poor underwater environments using PL-SLAM algorithm for localization. Three experiments were performed, including walking along the wall of a pool, walking along a linear route, and walking along an irregular route. The experimental results showed that the algorithm was highly robust in underwater low-texture environments due to the inclusion of line segments. At the same time, the algorithm achieved a high accuracy of location effectively. The attitude error--computed as shown in Equation (8)--was 0.1489 m, which represented the 2.98% DT. \[\text{Attitude error}\ =\ \sqrt{\left(\text{error\_x}\right)^{2}+\ \left(\text{error\_y}\right)^{2}+\ \left(\text{error\_z}\right)^{2}} \tag{9}\]  
Authors conclude that it can be implemented in the navigation and path planning of AUVs in the future. With the aim to explore the capabilities of visual-based SLAM in real and challenging environments, [PERSON] et al. [84] proposed what they considered as the first underwater dataset dedicated to the study of underwater localization methods from low-cost sensors. The dataset has been recorded in a harbor and provides several sequences with synchronized measurements from a monocular camera, a Micro-Electro-Mechanical System-Inertial Measurement Unit (MEMS-IMU) and a Pressure Sensor (PS). Among the sensors used in the dataset acquisition were a 20 frames per second (fps), 600 \(\times\) 512 px monochromatic camera, and a 200 Hz IMU. As a benchmark, authors ran experiments using state-of-the-art monocular SLAM algorithms, and then compared ORB-SLAM, Semi-direct Visual Odometry (SVO) and Direct Sparse Odometry (DSO). Results showed an absolute translation error between 24-52 cm, 24-67 cm, and 2-56 cm for each of the methods applied, which highlighted the potential of vision-based localization methods for underwater environments. With the same idea, [PERSON] et al.