Generation of 3D object and environment models with an imaging sonar

使用成像声纳生成 3D 对象和环境模型

• 批准号: 535678995
• 负责人: Professor Dr. Andreas Birk
• 金额: --
• 依托单位: Chair of Computer Science
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/535678995?language=en
• 关键词: Generation; 3D; object; environment; models

When employing underwater robots, 3D object and environment information are useful mission results and they also form an important basis for machine intelligence and agency, i.e. the use of (semi-)autonomous systems. Various optical methods are already very successfully used for the generation of underwater 3D data. However, the necessary visibility conditions are hardly or even not at all given in many environments. For sonar as an alternative, the generation of 3D data is in contrast still a major scientific challenge. In this project, the generation of 3D information from the 2D data of an imaging sonar, also known as an acoustic camera, is explored. In contrast to what the name suggests, the projection function of this type of sonar is very different from that of an optical camera. The data is arranged on one of the two image axes by distance, rather than by angle as in the pinhole camera model. Multiple points that can be placed very differently in the scene can hence be projected onto one point in the image. Thus, in 3D environments or with objects, there is for example considerable distortion compared to what is expected according to human vision. As further drawbacks, these sonar images have quite low resolution and they are heavily affected by noise. The resulting challenges are addressed as follows. First, a reformulation of the well-known optical method of bundle adjustment for the special projective geometry of an imaging sonar is developed. In particular, the ambiguities that arise are represented and taken into account in the optimization. Furthermore, the additional fourth degree of freedom (scale) of spectral registration methods is used as an additional constraint. Second, new methods are explored that allow robust registration with four degrees of freedom (translation, rotation, scale) of very noisy 2D data. In particular, the use of these frequency-based methods as image features, i.e., for finding correspondences between points in sonar images, is also explored. The objective is to achieve a quality in the 3D data that goes far beyond the state of the art in research, thus laying the foundations that will allow our approach to be used in relevant scenarios. All developed methods are therefore also intensively evaluated in real systems in realistic environments.

当使用水下机器人时，3D对象和环境信息是有用的使命结果，并且它们也形成机器智能和代理的重要基础，即使用（半）自主系统。各种光学方法已经非常成功地用于生成水下3D数据。然而，在许多环境中，几乎没有或根本没有给出必要的可见性条件。对于声纳作为替代方案，3D数据的生成相比之下仍然是一个重大的科学挑战。在这个项目中，从成像声纳（也称为声学相机）的2D数据生成3D信息进行了探索。顾名思义，这种声纳的投影功能与光学相机有很大的不同。数据按距离排列在两个图像轴之一上，而不是像针孔相机模型那样按角度排列。因此，可以将在场景中放置非常不同的多个点投影到图像中的一个点上。因此，在3D环境中或对于对象，与根据人类视觉所预期的相比，存在例如相当大的失真。作为进一步的缺点，这些声纳图像具有相当低的分辨率，并且它们受到噪声的严重影响。现将由此产生的挑战处理如下。首先，著名的光束法平差的成像声纳的特殊投影几何的重新制定。特别是，在优化中会表示并考虑出现的模糊性。此外，光谱配准方法的附加第四自由度（尺度）被用作附加约束。其次，新的方法进行了探索，允许强大的注册与四个自由度（平移，旋转，缩放）非常嘈杂的2D数据。特别地，使用这些基于频率的方法作为图像特征，即，用于寻找声纳图像中点之间的对应关系，也进行了探索。我们的目标是实现3D数据的质量，远远超出研究领域的最新水平，从而为我们的方法在相关场景中使用奠定基础。因此，所有开发的方法也在现实环境中的真实的系统中进行了深入评估。