Underwater inspection using sonar-based volumetric submaps

We propose a submap-based technique for mapping of underwater structures with complex geometries. Our approach relies on the use of probabilistic volumetric techniques to create submaps from multibeam sonar scans, as these offer increased outlier robustness. Special attention is paid to the problem of denoising/enhancing sonar data. Pairwise submap alignment constraints are used in a factor graph framework to correct for navigation drift and improve map accuracy. We provide experimental results obtained from the inspection of the running gear and bulbous bow of a 600-foot, Wright-class supply ship.United States. Office of Naval Research (N00014-12-1-0093)United States. Office of Naval Research (N00014-14-1-0373

Toward autonomous underwater mapping in partially structured 3D environments

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2014Motivated by inspection of complex underwater environments, we have developed a system for multi-sensor SLAM utilizing both structured and unstructured environmental features. We present a system for deriving planar constraints from sonar data, and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We also present a system for outlier rejection and smoothing of 3D sonar data, and for generating loop closure constraints based on the alignment of smoothed submaps. Our factor graph SLAM backend combines loop closure constraints from sonar data with detections of visual fiducial markers from camera imagery, and produces an online estimate of the full vehicle trajectory and landmark positions. We evaluate our technique on an inspection of a decomissioned aircraft carrier, as well as synthetic data and controlled indoor experiments, demonstrating improved trajectory estimates and reduced reprojection error in the final 3D map

Neural Implicit Surface Reconstruction using Imaging Sonar

We present a technique for dense 3D reconstruction of objects using an imaging sonar, also known as forward-looking sonar (FLS). Compared to previous methods that model the scene geometry as point clouds or volumetric grids, we represent the geometry as a neural implicit function. Additionally, given such a representation, we use a differentiable volumetric renderer that models the propagation of acoustic waves to synthesize imaging sonar measurements. We perform experiments on real and synthetic datasets and show that our algorithm reconstructs high-fidelity surface geometry from multi-view FLS images at much higher quality than was possible with previous techniques and without suffering from their associated memory overhead.Comment: 8 pages, 8 figures. This paper is under revie

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

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

Improving Self-Consistency in Underwater Mapping Through Laser-Based Loop Closure (Extended)

Accurate, self-consistent bathymetric maps are needed to monitor changes in subsea environments and infrastructure. These maps are increasingly collected by underwater vehicles, and mapping requires an accurate vehicle navigation solution. Commercial off-the-shelf (COTS) navigation solutions for underwater vehicles often rely on external acoustic sensors for localization, however survey-grade acoustic sensors are expensive to deploy and limit the range of the vehicle. Techniques from the field of simultaneous localization and mapping, particularly loop closures, can improve the quality of the navigation solution over dead-reckoning, but are difficult to integrate into COTS navigation systems. This work presents a method to improve the self-consistency of bathymetric maps by smoothly integrating loop-closure measurements into the state estimate produced by a commercial subsea navigation system. Integration is done using a white-noise-on-acceleration motion prior, without access to raw sensor measurements or proprietary models. Improvements in map self-consistency are shown for both simulated and experimental datasets, including a 3D scan of an underwater shipwreck in Wiarton, Ontario, Canada.Comment: 26 pages, 18 figures. V2 correct Table III x2 parameter values, Table VIII 'INS' values, and equation A.2

Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping

Underwater robots typically rely on acoustic sensors like sonar to perceive their surroundings. However, these sensors are often inundated with multiple sources and types of noise, which makes using raw data for any meaningful inference with features, objects, or boundary returns very difficult. While several conventional methods of dealing with noise exist, their success rates are unsatisfactory. This paper presents a novel application of conditional Generative Adversarial Networks (cGANs) to train a model to produce noise-free sonar images, outperforming several conventional filtering methods. Estimating free space is crucial for autonomous robots performing active exploration and mapping. Thus, we apply our approach to the task of underwater occupancy mapping and show superior free and occupied space inference when compared to conventional methods.Comment: 7 pages, 13 figures. This paper is published at 2023 IEEE International Conference on Robotics and Automation (ICRA

Mapping of complex marine environments using an unmanned surface craft

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 185-199).Recent technology has combined accurate GPS localization with mapping to build 3D maps in a diverse range of terrestrial environments, but the mapping of marine environments lags behind. This is particularly true in shallow water and coastal areas with man-made structures such as bridges, piers, and marinas, which can pose formidable challenges to autonomous underwater vehicle (AUV) operations. In this thesis, we propose a new approach for mapping shallow water marine environments, combining data from both above and below the water in a robust probabilistic state estimation framework. The ability to rapidly acquire detailed maps of these environments would have many applications, including surveillance, environmental monitoring, forensic search, and disaster recovery. Whereas most recent AUV mapping research has been limited to open waters, far from man-made surface structures, in our work we focus on complex shallow water environments, such as rivers and harbors, where man-made structures block GPS signals and pose hazards to navigation. Our goal is to enable an autonomous surface craft to combine data from the heterogeneous environments above and below the water surface - as if the water were drained, and we had a complete integrated model of the marine environment, with full visibility. To tackle this problem, we propose a new framework for 3D SLAM in marine environments that combines data obtained concurrently from above and below the water in a robust probabilistic state estimation framework. Our work makes systems, algorithmic, and experimental contributions in perceptual robotics for the marine environment. We have created a novel Autonomous Surface Vehicle (ASV), equipped with substantial onboard computation and an extensive sensor suite that includes three SICK lidars, a Blueview MB2250 imaging sonar, a Doppler Velocity Log, and an integrated global positioning system/inertial measurement unit (GPS/IMU) device. The data from these sensors is processed in a hybrid metric/topological SLAM state estimation framework. A key challenge to mapping is extracting effective constraints from 3D lidar data despite GPS loss and reacquisition. This was achieved by developing a GPS trust engine that uses a semi-supervised learning classifier to ascertain the validity of GPS information for different segments of the vehicle trajectory. This eliminates the troublesome effects of multipath on the vehicle trajectory estimate, and provides cues for submap decomposition. Localization from lidar point clouds is performed using octrees combined with Iterative Closest Point (ICP) matching, which provides constraints between submaps both within and across different mapping sessions. Submap positions are optimized via least squares optimization of the graph of constraints, to achieve global alignment. The global vehicle trajectory is used for subsea sonar bathymetric map generation and for mesh reconstruction from lidar data for 3D visualization of above-water structures. We present experimental results in the vicinity of several structures spanning or along the Charles River between Boston and Cambridge, MA. The Harvard and Longfellow Bridges, three sailing pavilions and a yacht club provide structures of interest, having both extensive superstructure and subsurface foundations. To quantitatively assess the mapping error, we compare against a georeferenced model of the Harvard Bridge using blueprints from the Library of Congress. Our results demonstrate the potential of this new approach to achieve robust and efficient model capture for complex shallow-water marine environments. Future work aims to incorporate autonomy for path planning of a region of interest while performing collision avoidance to enable fully autonomous surveys that achieve full sensor coverage of a complete marine environment.by Jacques Chadwick Leedekerken.Ph.D