Relocating Underwater Features Autonomously Using Sonar-Based SLAM
Relocating Underwater Features Autonomously Using Sonar-Based SLAM
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DOI：
10.1109/joe.2012.2235664
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发表时间：
2013-07-01
影响因子：
4.1
通讯作者：
Leonard, John J.
中科院分区：
文献类型：
--
作者：
Fallon, Maurice F.;Folkesson, John;Leonard, John J.
本文描述了一种使用配备低成本声纳和导航传感器的自主水下航行器（AUV）重新获取浅水海洋环境中感兴趣特征的系统。在执行水雷反制措施时，至关重要的是使 AUV 能够准确导航到先前绘制的水柱或海床上感兴趣的物体，以进行进一步的评估或补救。整个系统设计的一个重要方面是保持重新采办车辆的尺寸和成本尽可能低，因为它可能在重新采办任务中被摧毁。这种低成本要求阻碍了复杂的 AUV 导航传感器的使用，例如多普勒速度计程仪 (DVL) 或惯性导航系统 (INS)。相反，我们的系统使用 Blueview Technologies 的 Proviewer 900-kHz 成像声纳，该声纳可在约 4 Hz 的情况下生成距离达 40 m 的前视声纳 (FLS) 图像。希望这种传感器能够以低成本大批量生产。我们的方法使用一种新颖的同步定位和建图 (SLAM) 算法，该算法可以检测并跟踪 FLS 图像中的特征，以重新导航到之前映射的目标。这种基于特征的导航 (FBN) 系统融合了 SLAM 位姿图优化算法的许多最新进展。该系统经过了四年多的广泛现场测试，展示了使用这种新方法重新获取特征的潜力。在本报告中，我们回顾了 FBN 系统的方法和组件，描述了系统的技术特征，回顾了系统在一系列广泛的水下现场测试中的性能，并强调了未来研究的问题。
This paper describes a system for reacquiring features of interest in a shallow-water ocean environment, using autonomous underwater vehicles (AUVs) equipped with low-cost sonar and navigation sensors. In performing mine countermeasures, it is critical to enable AUVs to navigate accurately to previously mapped objects of interest in the water column or on the seabed, for further assessment or remediation. An important aspect of the overall system design is to keep the size and cost of the reacquisition vehicle as low as possible, as it may potentially be destroyed in the reacquisition mission. This low-cost requirement prevents the use of sophisticated AUV navigation sensors, such as a Doppler velocity log (DVL) or an inertial navigation system (INS). Our system instead uses the Proviewer 900-kHz imaging sonar from Blueview Technologies, which produces forward-looking sonar (FLS) images at ranges up to 40 m at approximately 4 Hz. In large volumes, it is hoped that this sensor can be manufactured at low cost. Our approach uses a novel simultaneous localization and mapping (SLAM) algorithm that detects and tracks features in the FLS images to renavigate to a previously mapped target. This feature-based navigation (FBN) system incorporates a number of recent advances in pose graph optimization algorithms for SLAM. The system has undergone extensive field testing over a period of more than four years, demonstrating the potential for the use of this new approach for feature reacquisition. In this report, we review the methodologies and components of the FBN system, describe the system's technological features, review the performance of the system in a series of extensive in-water field tests, and highlight issues for future research.