Low cost underwater acoustic localization

Over the course of the last decade, the cost of marine robotic platforms has significantly decreased. In part this has lowered the barriers to entry of exploring and monitoring larger areas of the earth's oceans. However, these advances have been mostly focused on autonomous surface vehicles (ASVs) or shallow water autonomous underwater vehicles (AUVs). One of the main drivers for high cost in the deep water domain is the challenge of localizing such vehicles using acoustics. A low cost one-way travel time underwater ranging system is proposed to assist in localizing deep water submersibles. The system consists of location aware anchor buoys at the surface and underwater nodes. This paper presents a comparison of methods together with details on the physical implementation to allow its integration into a deep sea micro AUV currently in development. Additional simulation results show error reductions by a factor of three.Comment: 73rd Meeting of the Acoustical Society of Americ

Mapping Complex Marine Environments with Autonomous Surface Craft

This paper presents a novel marine mapping system using an Autonomous Surface Craft (ASC). The platform includes an extensive sensor suite for mapping environments both above and below the water surface. A relatively small hull size and shallow draft permits operation in cluttered and shallow environments. We address the Simultaneous Mapping and Localization (SLAM) problem for concurrent mapping above and below the water in large scale marine environments. Our key algorithmic contributions include: (1) methods to account for degradation of GPS in close proximity to bridges or foliage canopies and (2) scalable systems for management of large volumes of sensor data to allow for consistent online mapping under limited physical memory. Experimental results are presented to demonstrate the approach for mapping selected structures along the Charles River in Boston.United States. Office of Naval Research (N00014-06-10043)United States. Office of Naval Research (N00014-05-10244)United States. Office of Naval Research (N00014-07-11102)Massachusetts Institute of Technology. Sea Grant College Program (grant 2007-R/RCM-20

Towards Autonomous Ship Hull Inspection using the Bluefin HAUV

URL is to paper listed on conference scheduleIn this paper we describe our effort to automate ship hull inspection for security applications. Our main contribution is a system that is capable of drift-free self-localization on a ship hull for extended periods of time. Maintaining accurate localization for the duration of a mission is important for navigation and for ensuring full coverage of the area to be inspected. We exclusively use onboard sensors including an imaging sonar to correct for drift in the vehicle’s navigation sensors. We present preliminary results from online experiments on a ship hull. We further describe ongoing work including adding capabilities for change detection by aligning vehicle trajectories of different missions based on a technique recently developed in our lab.United States. Office of Naval Research (grant N00014-06- 10043

The simultaneous localization and mapping (SLAM):An overview

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality

Experiments on Surface Reconstruction for Partially Submerged Marine Structures

Over the past 10 years, significant scientific effort has been dedicated to the problem of three-dimensional (3-D) surface reconstruction for structural systems. However, the critical area of marine structures remains insufficiently studied. The research presented here focuses on the problem of 3-D surface reconstruction in the marine environment. This paper summarizes our hardware, software, and experimental contributions on surface reconstruction over the past few years (2008–2011). We propose the use of off-the-shelf sensors and a robotic platform to scan marine structures both above and below the waterline, and we develop a method and software system that uses the Ball Pivoting Algorithm (BPA) and the Poisson reconstruction algorithm to reconstruct 3-D surface models of marine structures from the scanned data. We have tested our hardware and software systems extensively in Singapore waters, including operating in rough waters, where water currents are around 1–2 m/s. We present results on construction of various 3-D models of marine structures, including slowly moving structures such as floating platforms, moving boats, and stationary jetties. Furthermore, the proposed surface reconstruction algorithm makes no use of any navigation sensor such as GPS, a Doppler velocity log, or an inertial navigation system.Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin

Underwater Acoustic Localization with Applications to Multiuser Communications

Multiuser underwater acoustic communications (UACs) have gained attention because of a number of applications. To assess the performance of multiuser UACs and reduce the cost of experiments, simulations of the signal transmission are used. However, the existing underwater signal transmission simulators suffer from complexity and signal length limitation when investigating multiuser UACs. Therefore, it is useful to develop a signal transmission simulator for UACs. To improve the performance and bandwidth efficiency of multi-user systems, arrays can be used at the transmitter with transmit beamforming, which require the channel state information (CSI) available at the receiver to be sent as a feedback message to the transmitter. A long feedback message in UAC is a waste of the throughput and sometimes impractical. Therefore, it is important to develop an advanced transmit beamforming method for the multiple transmit sensor array systems. In this thesis, an underwater channel simulator based on acoustic field computation is proposed. We pre-compute and store the acoustic field in the investigation area, thus speeding up the computation whilst maintaining the performance. Based on this, an underwater receiver localization method is then proposed. In the localization, the CSIs at specific points in the investigation area are pre-computed and compared with the CSI measured at the receiver, thus the position of the receiver is estimated as the point with the best match. It offers a more practical solution to the underwater localization problem. A receiver trajectory estimation technique combining the proposed localization and smoothing approach is also proposed to reduce the cost of infrastructure, and it can be applied in a two-dimensional plane and a three-dimensional space. An advanced beamforming technique is introduced in the transmitter design based on the proposed localization technique. It offers accurate detection performance and the length of the feedback message is reduced significantly

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

Toward autonomous exploration in confined underwater environments

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 33 (2016): 994-1012, doi:10.1002/rob.21640.In this field note we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic sonar to simultaneously map the caves’ horizontal and vertical surfaces. Although the caves’ spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and mapping (SLAM) framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or pre-deployment of localization equipment are not feasible and may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.This research work was partially sponsored by the EU FP7-Projects: Tecniospring- Marie Curie (TECSPR13-1-0052), MORPH (FP7-ICT-2011-7-288704), Eurofleets2 (FP7-INF-2012-312762), and the National Science Foundation (OCE-0955674)