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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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A Modular Sensor Suite for Underwater Reconstruction
This paper presents the design, development, and application of a sensor suite, made with the explicit purpose of localizing and mapping in underwater environments. The design objectives of such an underwater sensor rig include simplicity of carrying, ease of operation in different modes, and data collection. The rig is equipped with stereo camera, inertial measurement unit (IMU), mechanical scanning sonar, and depth sensor. The electronics are enclosed in a water-proof PVC tube tested to sixty meters. The contribution of this paper is twofold: first, we open-source the design providing detailed instructions that are made available online; second, we discuss lessons learned as well as some successful applications where the presented sensor suite has been operated by divers.
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- Award ID(s):
- 1637876
- PAR ID:
- 10085397
- Date Published:
- Journal Name:
- OCEANS 2018 MTS/IEEE Charleston
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/OCEANS.2018.8604819
