# Monocular Vision Guidance for Unmanned Surface Vehicle Recovery
## 1 Introduction  
Unmanned surface vehicles (USVs) are self-driving tools capable of performing tasks in a variety of complex environments without any human intervention. USVs exhibit significant practical value in both commercial and military applications [1]. In commercial sectors, they are utilized in environmental monitoring [2], resource exploration [3], shipping [4], and other related areas. In the military domain, they can execute missions such as mine countermeasures [5], antisubmarine warfare [6], and reconnaissance [7], among others. Autonomous recovery and guidance are essential capabilities for USVs. While GPS positioning can guide USVs to approach the mother ship when they are at a distance, it lacks precision when USVs are in close proximity. To address this issue, employing visual positioning enables more precise guidance for USVs to return to the mother ship, especially in close-range scenarios. In recent years, visual guidance technology has gained significant attention in the field of computer vision and has been widely applied in domains such as autonomous driving [8], unmanned drone landing [9], robot guidance [10], and automatically guided vehicle (AGV) visual guidance [11]. Visual sensors can capture rich environmental scene information, enabling the precise positioning and guidance of targets. Additionally, they are cost-effective and user-friendly, making them highly favored in both academic and industrial sectors [12, 13, 14]. Visual guidance is typically based on monocular vision models or multi-vision models for the positioning of the target, and on using the obtained position and posture of the target to guide its movement [15]. Multi-vision requires multiple cameras for shooting, resulting in higher costs, and also requires addressing the feature matching issue from different cameras, which leads to complex operations [16, 17]. In contrast, monocular vision only requires a single camera, enabling implementation through the pinhole imaging principle, resulting in lower costs and convenient operation [18, 19]. The commonly used methods for monocular vision positioning include the Perspective-n-Point (PNP) method [20], theimaging model method [21], the data regression modeling method [22], and the geometric relationship method [23]. The imaging model method achieves positioning based on a similar triangle relationship and is typically utilized for forward vehicle positioning in the transportation field but is not suitable for directional shifts of water surface targets. The data regression method achieves positioning based on the mapping relationship model constructed from the pixel coordinates of feature points and actual coordinate data. This method requires a large amount of training data and is significantly influenced by the environment, thus not being suitable for water surface target positioning. The geometric relationship method establishes a model based on the known vertical height of the camera installation to achieve positioning, enabling accurate positioning of targets within a plane. However, water wave fluctuations can lead to significant positioning errors, making it unsuitable for water surfaces with pronounced ripples. The PNP method is a common technique in monocular vision positioning and guidance technology. It achieves positioning and guidance by identifying artificial markers and utilizing the geometric features of these markers. Therefore, this paper adopts the PNP method for the positioning and recovery guidance of unmanned surface vessels. Visual positioning and guidance based on artificial markers have a wide range of applications. [PERSON] [24] designed an artificial marker that consists of a circle and the letter \"H\". They used a model-based enhancement approach to improve the quality and brightness of the target image. This enabled safe landing of unmanned drones in low-light conditions. [PERSON] [25] developed a specialized ArCuo marker, which comprises black or white square blocks measuring \(7\times 7\) in size.