Experimental Comparison of Open Source VisualInertialBased State Estimation Algorithms in the Underwater Domain A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent opensource packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct and indirect methods that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor This paper presents a novel tightlycoupled keyframebased Simultaneous Localization and Mapping (SLAM) system with loopclosing and relocalization capabilities targeted for the underwater domain.Our previous work, SVIn, augmented the stateoftheart visualinertial state estimation package OKVIS to accommodate acoustic data from sonar in a nonlinear optimizationbased framework. This paper addresses drift and loss of localization xe2x80x93 one of the main problems affecting other packages in underwater domain xe2x80x93 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 realtime loopclosing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightlycoupled optimization formulation. Experimental results on datasets collected with a custommade 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.