Building 3D mosaics from an Autonomous Underwater Vehicle, Doppler velocity log, and 2D imaging sonar

AbstractThis paper reports on a 3D photomosaicing pipeline using data collected from an autonomous underwa-ter vehicle performing simultaneous localization and mapping (SLAM). The pipeline projects and blends 2D imaging sonar data onto a large-scale 3D mesh that is either given a priori or derived from SLAM. Compared to other methods that generate a 2D-only mosaic, our approach produces 3D models that are more structurally representative of the environment being surveyed. Additionally, our system leverages recent work in underwater SLAM using sparse point clouds derived from Doppler velocity log range returns to relax the need for a prior model. We show that the method produces reasonably accurate surface reconstruction and blending consistency, with and without the use of a prior mesh. We experimentally evaluate our approach with a Hovering Autonomous Underwater Vehicle (HAUV) performing inspection of a large underwater ship hull. I

Advances in Simultaneous Localization and Mapping in Confined Underwater Environments Using Sonar and Optical Imaging.

This thesis reports on the incorporation of surface information into a probabilistic simultaneous localization and mapping (SLAM) framework used on an autonomous underwater vehicle (AUV) designed for underwater inspection. AUVs operating in cluttered underwater environments, such as ship hulls or dams, are commonly equipped with Doppler-based sensors, which---in addition to navigation---provide a sparse representation of the environment in the form of a three-dimensional (3D) point cloud. The goal of this thesis is to develop perceptual algorithms that take full advantage of these sparse observations for correcting navigational drift and building a model of the environment. In particular, we focus on three objectives. First, we introduce a novel representation of this 3D point cloud as collections of planar features arranged in a factor graph. This factor graph representation probabalistically infers the spatial arrangement of each planar segment and can effectively model smooth surfaces (such as a ship hull). Second, we show how this technique can produce 3D models that serve as input to our pipeline that produces the first-ever 3D photomosaics using a two-dimensional (2D) imaging sonar. Finally, we propose a model-assisted bundle adjustment (BA) framework that allows for robust registration between surfaces observed from a Doppler sensor and visual features detected from optical images. Throughout this thesis, we show methods that produce 3D photomosaics using a combination of triangular meshes (derived from our SLAM framework or given a-priori), optical images, and sonar images. Overall, the contributions of this thesis greatly increase the accuracy, reliability, and utility of in-water ship hull inspection with AUVs despite the challenges they face in underwater environments. We provide results using the Hovering Autonomous Underwater Vehicle (HAUV) for autonomous ship hull inspection, which serves as the primary testbed for the algorithms presented in this thesis. The sensor payload of the HAUV consists primarily of: a Doppler velocity log (DVL) for underwater navigation and ranging, monocular and stereo cameras, and---for some applications---an imaging sonar.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120750/1/paulozog_1.pd

Place Recognition and Localization for Multi-Modal Underwater Navigation with Vision and Acoustic Sensors

Place recognition and localization are important topics in both robotic navigation and computer vision. They are a key prerequisite for simultaneous localization and mapping (SLAM) systems, and also important for long-term robot operation when registering maps generated at different times. The place recognition and relocalization problem is more challenging in the underwater environment because of four main factors: 1) changes in illumination; 2) long-term changes in the physical appearance of features in the aqueous environment attributable to biofouling and the natural growth, death, and movement of living organisms; 3) low density of reliable visual features; and 4) low visibility in a turbid environment. There is no one perceptual modality for underwater vehicles that can single-handedly address all the challenges of underwater place recognition and localization. This thesis proposes novel research in place recognition methods for underwater robotic navigation using both acoustic and optical imaging modalities. We develop robust place recognition algorithms using both optical cameras and a Forward-looking Sonar (FLS) for an active visual SLAM system that addresses the challenges mentioned above. We first design an optical image matching algorithm using high-level features to evaluate image similarity against dramatic appearance changes and low image feature density. A localization algorithm is then built upon this method combining both image similarity and measurements from other navigation sensors, which enables a vehicle to localize itself to maps temporally separated over the span of years. Next, we explore the potential of FLS in the place recognition task. The weak feature texture and high noise level in sonar images increase the difficulty in making correspondences among them. We learn descriptive image-level features using a convolutional neural network (CNN) with the data collected for our ship hull inspection mission. These features present outstanding performance in sonar image matching, which can be used for effective loop-closure proposal for SLAM as well as multi-session SLAM registration. Building upon this, we propose a pre-linearization approach to leverage this type of general high-dimensional abstracted feature in a real-time recursive Bayesian filtering framework, which results in the first real-time recursive localization framework using this modality. Finally, we propose a novel pose-graph SLAM algorithm leveraging FLS as the perceptual sensors providing constraints for drift correction. In this algorithm, we address practical problems that arise when using an FLS for SLAM, including feature sparsity, low reliability in data association and geometry estimation. More specifically, we propose a novel approach to pruning out less-informative sonar frames that improve system efficiency and reliability. We also employ local bundle adjustment to optimize the geometric constraints between sonar frames and use the mechanism to avoid degenerate motion patterns. All the proposed contributions are evaluated with real-data collected for ship hull inspection. The experimental results outperform existent benchmarks. The culmination of these contributions is a system capable of performing underwater SLAM with both optical and acoustic imagery gathered across years under challenging imaging conditions.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140835/1/ljlijie_1.pd

Dense, sonar-based reconstruction of underwater scenes

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.Three-dimensional maps of underwater scenes are critical to—or the desired end product of—many applications, spanning a spectrum of spatial scales. Examples range from inspection of subsea infrastructure to hydrographic surveys of coastlines. Depending on the end use, maps will have different accuracy requirements. The accuracy of a mapping platform depends mainly on the individual accuracies of (i) its pose estimate in some global frame, (ii) the estimates of offsets between mapping sensors and platform, and (iii) the accuracy of the mapping sensor measurements. Typically, surface-based surveying platforms will employ highly accurate positioning sensors—e.g. a combination of differential global navigation satellite system (GNSS) receiver with an accurate attitude and heading reference system—to instrument the pose of a mapping sensor such as a multibeam sonar. For underwater platforms, the rapid attenuation of electromagnetic signals in water precludes the use of GNSS receivers at any meaningful depth. Acoustic positioning systems, the underwater analogues to GNSS, are limited to small survey areas and free of obstacles that may result in undesirable acoustic effects such as multi-path propagation and reverberation. Save for a few exceptions, the accuracy and update rate of these systems is significantly lower than that of differential GNSS. This performance reduction shifts the accuracy burden to inertial navigation systems (INS), often aided by Doppler velocity logs. Still, the pose estimates of an aided INS will incur in unbounded drift growth over time, often necessitating the use of techniques such as simultaneous localization and mapping (SLAM) to leverage local features to bound the uncertainty in the position estimate. The contributions presented in this dissertation aim at improving the accuracy of maps of underwater scenes produced from multibeam sonar data. First, we propose robust methods to process and segment sonar data to obtain accurate range measurements in the presence of noise, sensor artifacts, and outliers. Second, we propose a volumetric, submap-based SLAM technique that can successfully leverage map information to correct for drift in the mapping platform’s pose estimate. Third, and informed by the previous two contributions, we propose a dense approach to the sonar-based reconstruction problem, in which the pose estimation, sonar segmentation and model optimization problems are tackled simultaneously under the unified framework of factor graphs. This stands in contrast with the traditional approach where the sensor processing and segmentation, pose estimation, and model reconstruction problems are solved independently. Finally, we provide experimental results obtained over several deployments of a commercial inspection platform that validate the proposed techniques.This work was generously supported by the Office of Naval Research1, the MIT-Portugal Program, and the Schlumberger Technology Corporation

3D reconstruction and object recognition from 2D SONAR data

Accurate and meaningful representations of the environment are required for autonomy in underwater applications. Thanks to favourable propagation properties in water, acoustic sensors are commonly preferred to video cameras and lasers but do not provide direct 3D information. This thesis addresses the 3D reconstruction of underwater scenes from 2D imaging SONAR data as well as the recognition of objects of interest in the reconstructed scene. We present two 3D reconstruction methods and two model-based object recognition methods. We evaluate our algorithms on multiple scenarios including data gathered by an AUV. We show the ability to reconstruct underwater environments at centimetre-level accuracy using 2D SONARs of any aperture. We demonstrate the recognition of structures of interest on a medium-sized oil-field type environment providing accurate yet low memory footprint semantic world models. We conclude that accurate 3D semantic representations of partially-structured marine environments can be obtained from commonly embedded 2D SONARs, enabling online world modelling, relocalisation and model-based applications

3D Recording and Interpretation for Maritime Archaeology

This open access peer-reviewed volume was inspired by the UNESCO UNITWIN Network for Underwater Archaeology International Workshop held at Flinders University, Adelaide, Australia in November 2016. Content is based on, but not limited to, the work presented at the workshop which was dedicated to 3D recording and interpretation for maritime archaeology. The volume consists of contributions from leading international experts as well as up-and-coming early career researchers from around the globe. The content of the book includes recording and analysis of maritime archaeology through emerging technologies, including both practical and theoretical contributions. Topics include photogrammetric recording, laser scanning, marine geophysical 3D survey techniques, virtual reality, 3D modelling and reconstruction, data integration and Geographic Information Systems. The principal incentive for this publication is the ongoing rapid shift in the methodologies of maritime archaeology within recent years and a marked increase in the use of 3D and digital approaches. This convergence of digital technologies such as underwater photography and photogrammetry, 3D sonar, 3D virtual reality, and 3D printing has highlighted a pressing need for these new methodologies to be considered together, both in terms of defining the state-of-the-art and for consideration of future directions. As a scholarly publication, the audience for the book includes students and researchers, as well as professionals working in various aspects of archaeology, heritage management, education, museums, and public policy. It will be of special interest to those working in the field of coastal cultural resource management and underwater archaeology but will also be of broader interest to anyone interested in archaeology and to those in other disciplines who are now engaging with 3D recording and visualization