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1. Contour based Reconstruction of Underwater Structures Using Sonar, Visual, Inertial, and Depth Sensor

   Sharmin Rahman, Alberto Quattrini (October 2019, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper presents a systematic approach on realtime reconstruction of an underwater environment using Sonar, Visual, Inertial, and Depth data. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures, and the shadow contours. The proposed method utilizes the well defined edges between well lit areas and darkness to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a Visual SLAM system. Experimental results in an underwater cave at Ginnie Springs, FL, with a custommade underwater sensor suite demonstrate the performance of our system. This will enable more robust navigation of AUVs using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions. 

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2. SVIn2: A multi-sensor fusion-based underwater SLAM system

   https://doi.org/10.1177/02783649221110259  

   Rahman, Sharmin; Quattrini Li, Alberto; Rekleitis, Ioannis (July 2022, The International Journal of Robotics Research)

   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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3. An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

   Sharmin Rahman, Alberto Quattrini (October 2019, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain. Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization – one of the main problems affecting other packages in underwater domain – by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness. 

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4. Mapping the Catacombs: An Underwater Cave Segment of the Devil's Eye System

   Chatzispyrou, Michalis; Horgan, Luke; Hwang, Hyunkil; Sathishchandra, Harish; Roznere, Monika; Quattrini_Li, Alberto; Mordohai, Philippos; Rekleitis, Ioannis (May 2025, Workshop on Field Robotics - International Conference on Robotics and Automation)

   This paper presents a framework for mapping underwater caves. Underwater caves are crucial for fresh water resource management, underwater archaeology, and hydrogeology. Mapping the cave's outline and dimensions, as well as creating photorealistic 3D maps, is critical for enabling a better understanding of this underwater domain. In this paper, we present the mapping of an underwater cave segment (the catacombs) of the Devil's Eye cave system at Ginnie Springs, FL. We utilized a set of inexpensive action cameras in conjunction with a dive computer to estimate the trajectories of the cameras together with a sparse point cloud. The resulting reconstructions are utilized to produce a one-dimensional retract of the cave passages in the form of the average trajectory together with the boundaries (top, bottom, left, and right). The use of the dive computer enables the observability of the z-dimension in addition to the roll and pitch in a visual/inertial framework (SVIn2). In addition, the keyframes generated by SVIn2 together with the estimated camera poses for select areas are used as input to a global optimization (bundle adjustment) framework -- COLMAP -- in order to produce a dense reconstruction of those areas. The same cave segment is manually surveyed using the MNemo V2 instrument, providing an additional set of measurements validating the proposed approach. It is worth noting that with the use of action cameras, the primary components of a cave map can be constructed. Furthermore, with the utilization of a global optimization framework guided by the results of VI-SLAM package SVIn2, photorealistic dense 3D representations of selected areas can be reconstructed. The collected data will be made available upon publication of the paper. 

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5. Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots

   Kumar, Hans; Payne, J. Joe; Travers, Matthew; Johnson, Aaron M.; Choset, Howie (May 2022, IEEE International Conference on Robotics and Automation)

   Methods for state estimation that rely on visual information are challenging on legged robots due to rapid changes in the viewing angle of onboard cameras. In this work, we show that by leveraging structure in the way that the robot locomotes, the accuracy of visual-inertial SLAM in these challenging scenarios can be increased. We present a method that takes advantage of the underlying periodic predictability often present in the motion of legged robots to improve the performance of the feature tracking module within a visual-inertial SLAM system. Our method performs multi-session SLAM on a single robot, where each session is responsible for mapping during a distinct portion of the robot’s gait cycle. Our method produces lower absolute trajectory error than several state-of-the-art methods for visual-inertial SLAM in both a simulated environment and on data collected on a quadrupedal robot executing dynamic gaits. On real-world bounding gaits, our median trajectory error was less than 35% of the error of the next best estimate provided by state-of-the-art methods. 

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