The International Journal of Robotics Research · 2022 · 95 citations · 57 references
EngineeringOcean EngineeringRobotics EngineeringRoboticsSensor FusionUnderwater RoboticsAcoustic SensingSensor Fusion For LocalizationSimultaneous LocalizationSonar SystemsUnderwater LocalizationUnderwater PerceptionAutonomous Underwater Vehicle SystemsLocalization AlgorithmsRelocalizationUnderwater MappingUnderwater Sensor FusionUnderwater SlamUnderwater Visual SlamVisual-inertial Mapping
Visual‑inertial SLAM performs well outdoors and indoors but struggles underwater because of environmental challenges. The paper introduces SVIn2, a tightly‑coupled keyframe‑based SLAM system that fuses sonar, visual, inertial, and pressure data to overcome underwater SLAM challenges. SVIn2 tightly couples keyframes in a non‑linear optimization framework, integrating acoustic range, depth from pressure, visual, inertial, and sonar data to improve reconstruction, initialization, and drift reduction. The real‑time system demonstrates robust initialization, loop‑closing, and relocalization, achieving high accuracy and robustness in hazy, low‑light, and variable‑lighting underwater scenarios, and has been validated on benchmark datasets and real‑world missions, enabling tasks such as obstacle‑aware planning.
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.
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Parallel Tracking and Mapping for Small AR Workspaces
Georg Klein, David W. Murray · 2007 · 4.2K citations