This paper reports on a system for an autonomous underwater vehicle to perform in-situ, multiple session, hull inspection using long-term simultaneous localization and mapping (SLAM). Our method assumes very little a-priori knowledge, and does not require the aid of acoustic beacons for navigation, which is a typical mode of navigation in this type of application. Our system combines recent techniques in underwater saliency-informed visual SLAM and a method for representing the ship hull surface as a collection of many locally planar surface features. This methodology produces accurate maps that can be constructed in real-time on consumer-grade computing hardware. A single-session SLAM result is initially used as a prior map for later sessions, where the robot automatically merges the multiple surveys into a common hull-relative reference frame. To perform the re-localization step, we use a particle filter that leverages the locally planar representation of the ship hull surface, and a fast visual descriptor matching algorithm. Finally, we apply the recentlydeveloped graph sparsification tool, generic linear constraints (GLC), as a way to manage the computational complexity of the SLAM system as the robot accumulates information across multiple sessions. We show results for 20 SLAM sessions for two large vessels over the course of days, months, and even up to three years, with a total path length of approximately 10.2 km.

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Giancarlo Troni Pontificia Universidad Católica de Chile Paul Ozog University of Michigan Jon C. Henderson University of NottinghamDocumenting underwater archaeological sites is an extremely challenging problem. Sites covering large areas are particularly daunting for traditional techniques. In this paper, we present a novel approach to this problem using both an autonomous underwater vehicle (AUV) and a diver-controlled stereo imaging platform to document the submerged Bronze Age city at Pavlopetri, Greece. The result is a three-dimensional (3D) reconstruction covering 26,600 m2 at a resolution of 2 mm/pixel, the largest-scale underwater optical 3D map, at such a resolution, in the world to date. We discuss the advances necessary to achieve this result, including i) an approach to color correct large numbers of images at varying altitudes and over varying bottom types; ii) a large-scale bundle adjustment framework that is capable of handling upward of 400,000 stereo images; and iii) a novel approach to the registration and rapid documentation of an underwater excavations area that can quickly produce maps of site change. We present visual and quantitative comparisons to the authors' previous underwater mapping approaches. C 2016 Wiley Periodicals, Inc.

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Abstract-This paper reports on the use of planar patches as features in a real-time simultaneous localization and mapping (SLAM) system to model smooth surfaces as piecewise-planar. This approach works well for using observed point clouds to correct odometry error, even when the point cloud is sparse. Such sparse point clouds are easily derived by Doppler velocity log sensors for underwater navigation. Each planar patch contained in this point cloud can be constrained in a factorgraph-based approach to SLAM so that neighboring patches are sufficiently coplanar so as to constrain the robot trajectory, but not so much so that the curvature of the surface is lost in the representation. To validate our approach, we simulated a virtual 6-degree of freedom robot performing a spiral-like survey of a sphere, and provide real-world experimental results for an autonomous underwater vehicle used for automated ship hull inspection. We demonstrate that using the sparse 3D point cloud greatly improves the self-consistency of the map. Furthermore, the use of our piecewise-planar framework provides an additional constraint to multi-session underwater SLAM, improving performance over monocular camera measurements alone.

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