使用正交成像声纳生成的子图进行大规模密集3D建图
Large-Scale Dense 3D Mapping Using Submaps Derived From Orthogonal Imaging Sonars
AI总结：
针对水下声学传感器噪声高、3D信息缺失问题，提出与SLAM集成的子图建图系统，可对含不同密度简单重复目标的复杂未知环境生成密集3D地图，通过与先前工作对比验证其在水下态势感知中的进展。
摘要：
3D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，环境条件通常要求使用声学传感器。这些声学传感器存在噪声高以及声纳图像缺乏3D信息的问题，这促使人们使用正交成像声纳对来恢复3D感知数据。迄今为止，该领域的建图系统仅在离散时间步使用可用数据的子集，并且依赖环境中的目标级先验信息来构建高覆盖度的3D地图。此外，必须存在简单重复的目标才能构建高覆盖度地图。在这项工作中，我们提出了一种基于子图的建图系统，该系统与同时定位与建图（SLAM）系统集成，能够为具有不同密度简单重复目标的复杂未知环境生成密集的3D地图。我们将这种子图方法与我们在该领域的先前工作进行了比较，分析了简单和高度复杂的环境（例如水下飞机）。我们分析了基于子图的方法与我们先前利用简单重复目标的工作之间的权衡，展示了每种方法的适用场景和局限性。重要的是，我们提出的子图使用方法通过宽孔径多波束成像声纳在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大规模密集3D建图能力迈进。
英文摘要：
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.
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