SVIn2: A multi-sensor fusion-based underwater SLAM system
SVIn2: A multi-sensor fusion-based underwater SLAM system
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DOI：
10.1177/02783649221110259
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发表时间：
2022-07-13
影响因子：
9.2
通讯作者：
Rekleitis, Ioannis
中科院分区：
文献类型：
--
作者：
Rahman, Sharmin;Quattrini Li, Alberto;Rekleitis, Ioannis
SVIn2是一种新型的基于关键帧的紧密耦合同步定位和测绘（SLAM）系统，它将扫描剖面声呐、视觉、惯性和水压信息融合在一个非线性优化框架中，适用于小型和大型具有挑战性的水下环境。开发的实时系统具有强大的初始化、闭环和重新定位功能，这使得系统在雾霾、模糊、低光和光照变化的情况下可靠，通常在水下场景中观察到。在过去的十年中，视觉惯性里程计和SLAM系统在室内和室外环境下对移动机器人表现出了优异的性能，但由于在这种环境下固有的困难，往往在水下失效。我们的方法通过利用额外的传感器和利用它们的互补特性来克服以前方法的弱点。特别地，我们使用(1)声学距离信息来改进重建和定位，这得益于可靠的距离测量；(2)利用水压传感器的深度信息进行鲁棒初始化，细化尺度，并有助于限制紧耦合集成中的漂移。开发的开源软件已成功用于在基准数据集和许多真实水下场景中测试和验证所提出的系统，包括使用定制水下传感器套件和自主水下航行器Aqua2收集的数据集。SVIn2在这些数据集的准确性和鲁棒性方面表现出色，并支持其他机器人任务，例如，在存在障碍物的水下机器人进行规划。
This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software-made open source-has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.