# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
## 1 Introduction  
Underwater research has been evolving rapidly during the last few decades and autonomous underwater vehicles (AUVs), as an important part of underwater research, are widely used in harsh underwater environments instead of human exploration. However, it is a tough challenge to guarantee the underwater location accuracy of AUVs. Currently, many methods are used to position AUVs, such as inertial measurement units (IMUs), Doppler velocity logs (DVLs), pressure sensors, sonar and visual sensors. For example, [PERSON] et al. used a side-scan sonar and a forward-look sonar as perception sensors in an AUV for mine countermeasures and localization [1]. The graph was initialized by pose nodes from a GPS, and a nonlinear least square optimization was performed for the dead-reckoning (DVL and IMU) sensor and sonar images. However, sonars are susceptible to interference from the water surface and other sources of sound reflection in shallow water areas. To localize the AUV, inertial units (from an accelerometer or gyroscope), DVLs and pressure sensors are fused by Kalman filter [2]. However, this approach is prone to generating drift without a periodical correction based on a loop closing detection mechanism. AUVs needs to have good positioning accuracy in shallow water areas, and this paper introduces a periodic correction based on a closed-loop detection mechanism to further improve positioning accuracy. Therefore, the visual simultaneous localization and mapping (SLAM) localization method was chosen in this study. In shallow water areas, visual sensors are better than sonar because they cannot be affected by reflection. The SLAM [3] technique has proved to be one of the most popular and available methods to perform precise localization in unknown environments. When the AUV reaches a position that it has been to before, SLAM provides a loop detection mechanism that eliminates cumulative errors and drift. Although the quality of the picture will be seriously affected by the scattering and attenuation of underwater light and poor illumination conditions, cameras have larger spatial and temporal resolutions compared to acoustic sensors, which makes cameras more suitable for certain applications, such as surveying, object identification and intervention [4]. [PERSON] and [PERSON] proposed a computationally efficient approach that could be applied in visual simultaneous localization and mapping for the autonomous inspection of underwater structures using monocular vision [5]. [PERSON] et al. proposed a vision-based simultaneous localization and mapping of AUVs in which underwater artificial landmarks were used to help the visual sensing of forward- and downward-looking cameras [6]. [PERSON] and [PERSON] performed a visual SLAM using monocular video images and utilized a special saliency method using local and global saliency for feature detection in hull inspection [7]. [PERSON] et al. proposed the stereo graph-SLAM algorithm for the localization and navigation of underwater vehicles, which optimized the vehicle trajectory and processed the features from the graph [8]. [PERSON] et al. proposed a novel technique to detect loop closings visually in underwater environments to increase the accuracy of vision-based localization systems [9]. All of the above-mentioned visual SLAM methods are based on point feature localization. However, the above methods would cause instability of the system because of the low-texture in many underwater environments, which contain a small number of point features. However, there are rich planar elements in the linear shapes in many low-texture environments, from which line segment features can be extracted. Based on the oriented FAST (Features From Accelerated Segment Test) and rotated brief SLAM (ORB-SLAM) [10] framework, point and line SLAM PL-SLAM [11] can simultaneously utilize point and line information. As suggested in [12], lines are parameterized by their endpoints, the precise locations of which are estimated by following a two-step optimization process in the image plane.