Real-time side scan image generation and registration framework for AUV route following

P. King, A. Vardy, P. Vandrish, B. Anstey
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引用次数: 11

Abstract

Memorial University is in the development stages of a Qualitative Navigation System (QNS) to be deployed on the Memorial Explorer AUV. This system will allow localization and path following along a trained route without the necessity of a globally referenced position estimate. Previous QNS work has been on terrestrial robots using optical images. Our main challenge lies in utilization of side scan sonar as the imaging medium, as this type of sonar is prevalent on AUVs and provides much better range and coverage than optics in water. To achieve this, a sonar image processing and registration framework has been developed. To be useful such a framework should be fully-autonomous, robust, and operate in real-time, where real-time operation is defined as the ability to process, register and localize data at the rate it is collected, or faster. In this paper we describe our framework for processing sonar data, generating image tiles, extracting unique features and localizing against a reference set. We also present some results of this system based on raw sonar input data collected by the AUV.
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AUV航路跟踪的实时侧扫图像生成与配准框架
纪念大学正处于定性导航系统(QNS)的开发阶段,该系统将部署在纪念探索者AUV上。该系统将允许沿着训练路线进行定位和路径跟踪,而无需全局参考位置估计。以前的QNS工作是在地面机器人上使用光学图像。我们的主要挑战在于利用侧扫声纳作为成像介质,因为这种类型的声纳在auv上很普遍,并且提供比水中光学更好的范围和覆盖范围。为了实现这一目标,开发了声纳图像处理和配准框架。这样的框架应该是完全自主的、健壮的和实时操作的,其中实时操作被定义为以收集数据的速度或更快的速度处理、注册和本地化数据的能力。在本文中,我们描述了处理声纳数据、生成图像块、提取独特特征和根据参考集进行定位的框架。本文还介绍了该系统基于水下机器人采集的原始声纳输入数据的一些结果。
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