3D situational awareness is critical for any autonomous system. However, when operating underwater, environmental conditions often dictate the use of acoustic sensors. These acoustic sensors are plagued by high noise and a lack of 3D information in sonar imagery, motivating the use of an orthogonal pair of imaging sonars to recover 3D perceptual data. Thus far, mapping systems in this area only use a subset of the available data at discrete timesteps and rely on object-level prior information in the environment to develop high-coverage 3D maps. Moreover, simple repeating objects must be present to build high-coverage maps. In this work, we propose a submap-based mapping system integrated with a simultaneous localization and mapping (SLAM) system to produce dense, 3D maps of complex unknown environments with varying densities of simple repeating objects. We compare this submapping approach to our previous works in this area, analyzing simple and highly complex environments, such as submerged aircraft. We analyze the tradeoffs between a submapping-based approach and our previous work leveraging simple repeating objects. We show where each method is well-motivated and where they fall short. Importantly, our proposed use of submapping achieves an advance in underwater situational awareness with wide aperture multi-beam imaging sonar, moving toward generalized large-scale dense 3D mapping capability for fully unknown complex environments.

翻译：三维态势感知对于任何自主系统都至关重要。然而，在水下作业时，环境条件往往要求使用声学传感器。这些声学传感器受困于高噪声和声呐图像中三维信息的缺失，这促使我们采用一对正交的成像声呐来恢复三维感知数据。迄今为止，该领域的建图系统仅使用离散时间步长上的可用数据子集，并依赖环境中的物体级先验信息来构建高覆盖度的三维地图。此外，必须存在简单的重复物体才能构建高覆盖度的地图。在本工作中，我们提出了一种与同步定位与建图（SLAM）系统集成的基于子地图的建图系统，用于为具有不同简单重复物体密度的复杂未知环境生成密集的三维地图。我们将这种子地图方法与我们在该领域的先前工作进行比较，分析了简单和高度复杂的环境，例如沉没的飞机。我们分析了基于子地图的方法与我们先前利用简单重复物体的工作之间的权衡。我们展示了每种方法在何种情况下是合理的，以及在何处存在不足。重要的是，我们提出的子地图使用方法在使用宽孔径多波束成像声呐的水下态势感知方面取得了进展，朝着为完全未知的复杂环境实现通用的大规模密集三维建图能力迈进。