Underwater Vision For Intelligent Autonomous Underwater Vehicles

佛罗里达大西洋大学和卡内基梅隆大学提出了一项联合工作计划，为智能自主水下航行器开发水下三维视觉系统。这项工作的主要重点将是开发用于处理来自多元素声纳阵列的信号的算法，以生成海底地形的网格表示，以及开发地图构建算法，以生成地形的连贯地图，并将该表示用于导航、目标识别和分类以及路径规划。现有类型的声纳系统一般生成二维信息，如果生成三维信息，则分辨率相当差。因此，一种创新的声纳系统将被开发，它不同于其他声纳系统的操作模式，更多地依赖于数据处理，这将允许以高分辨率提取三维信息，并且仅由处理单元的速度控制刷新率。信号处理的基本方法是对声呐系统的多单元接收机阵列接收到的声信号进行多维离散傅里叶变换，并在时间和空间上进行变换。该处理将首先生成角度-角度-距离数据，然后将其转换为海底地形的离散表示。地图构建过程的主要任务将是数据处理和相对于地形从不同车辆位置和方向获取的数据结构的匹配，以及提取高级特征，如物体位置，物体类型和地形信息。结果是支持水下测量和基于传感器的导航所需的高水平三维世界模型图像。

A joint program of work is proposed between Florida Atlantic University and Carnegie-Mellon University to develop an underwater three dimensional vision system for intelligent autonomous underwater vehicles. The main thrust of the work will be in the development of algorithms for the processing of the signal from a multiple element SONAR array to generate a grid representation of the ocean bottom terrain and the development of map building algorithms to generate a coherent map of the terrain and use the representation for navigation, object recognition and classification, and path planning. The present types of SONAR systems in general generate two dimensional information or if three dimensional information is generated, the resolution is rather poor. Thus, an innovative SONAR system is to be developed that differs from other SONAR systems its in its mode of operation, relying more on data processing which will allow the extraction of three dimensional information with high resolution and with a refresh rate controlled only by the speed of the processing unit. The basic approach of the signal processing consists of multi-dimensional discrete Fourier transforms, in time and space, of the acoustic signals received by the multi-element receiver array of the SONAR system. The processing will first generate angle-angle-range data which is then converted to a discrete representation of the ocean bottom terrain. The main tasks of the map building process will be the data handling and the matching of data structures acquired from different vehicle locations and orientations relative to the terrain and the extraction of high level features such as object location, object type an topographical information. The result is a high level three dimensional world model image required to support underwater surveying and sensor based navigation.