In this article, a novel dense underwater 3-D mapping paradigm based on pose graph simultaneous localization and mapping (SLAM) using an acoustic camera mounted on a rotator is proposed. The demands of underwater tasks, such as unmanned construction using robots, are growing rapidly. In recent years, the acoustic camera, which is a state-of-the-art forward-looking imaging sonar, has been gradually applied in underwater exploration. However, distinctive imaging principles make it difficult to gain an intuitive perception of an underwater environment. In this study, an acoustic camera with a rotator was used for dense 3-D mapping of the underwater environment. The proposed method first applies a 3-D occupancy mapping framework based on the acoustic camera rotating around the acoustic axis using a rotator at a stationary position to generate 3-D local maps. Then, scan matching of adjacent local maps is implemented to calculate odometry without involving internal sensors, and an approximate dense global map is built in real time. Finally, based on a graph optimization scheme, offline refinement is performed to generate a final dense global map. Our experimental results demonstrate that our 3-D mapping framework for an acoustic camera can achieve dense 3-D mapping of underwater environments robustly and accurately.

中文翻译：

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用于水下环境中密集 3-D 映射的基于声学相机的姿态图 SLAM

在本文中，提出了一种基于位姿图同时定位和映射 (SLAM) 的新型密集水下 3-D 映射范式，该范式使用安装在旋转器上的声学摄像机。使用机器人进行无人施工等水下任务的需求正在迅速增长。近年来，声学相机作为一种先进的前视成像声纳，逐渐应用于水下探测。然而，独特的成像原理使其难以获得对水下环境的直观感知。在这项研究中，带有旋转器的声学相机用于水下环境的密集 3-D 映射。所提出的方法首先应用基于声学相机围绕声轴旋转的 3-D 占用映射框架，使用固定位置的旋转器来生成 3-D 局部地图。然后，在不涉及内部传感器的情况下，实现相邻局部地图的扫描匹配以计算里程计，并实时构建近似密集的全局地图。最后，基于图优化方案，进行离线细化以生成最终的密集全局图。我们的实验结果表明，我们用于声学相机的 3-D 映射框架可以稳健而准确地实现水下环境的密集 3-D 映射。基于图优化方案，进行离线细化以生成最终的密集全局图。我们的实验结果表明，我们用于声学相机的 3-D 映射框架可以稳健而准确地实现水下环境的密集 3-D 映射。基于图优化方案，进行离线细化以生成最终的密集全局图。我们的实验结果表明，我们用于声学相机的 3-D 映射框架可以稳健而准确地实现水下环境的密集 3-D 映射。