SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This addresses localization problems for underwater robotics in low-visibility conditions, representing an incremental improvement over their previous SVIn system.
The paper tackles drift and loss of localization in underwater SLAM by introducing a tightly-coupled system (SVIn2) that integrates sonar, visual, inertial, and depth sensors, achieving unprecedented accuracy and robustness in challenging underwater environments with poor visibility.
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. An additional contribution is the introduction 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.