Underwater Vision For Intelligent Autonomous Underwater Vehicles

佛罗里达大西洋公司提出了一项联合工作计划， 大学和卡内基梅隆大学开发一个 智能水下三维视觉系统 自主水下航行器 这项工作的主旨将是 在算法的发展过程中， 从多单元声纳阵列发出信号以产生网格 海洋底部地形的表示和 地图构建算法，以生成地形的连贯地图 并将其用于导航、物体识别和 分类和路径规划。 目前的声纳类型 系统通常生成二维信息，或者如果 生成三维信息时， 相当穷。 因此，需要开发一种创新的声纳系统 与其他声纳系统不同的是， 操作，更多地依赖于数据处理，这将允许 三维信息的提取， 分辨率，刷新率仅由速度控制 的处理单元。信号的基本方法 处理包括多维离散傅立叶 接收到的声信号的时间和空间变换 声纳系统的多单元接收器阵列。 的 处理将首先生成角度-角度-范围数据， 然后转换成海底的离散表示 地形地图构建过程的主要任务是 数据处理和数据结构的匹配， 不同的车辆位置和方向相对于 地形和提取高层次的特征，如对象 位置、对象类型和地形信息。 结果 是需要的高级三维世界模型图像， 支持水下测量和基于传感器的导航。

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.