247 research outputs found

    Self consistent bathymetric mapping from robotic vehicles in the deep ocean

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    Submitted In partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution June 2005Obtaining accurate and repeatable navigation for robotic vehicles in the deep ocean is difficult and consequently a limiting factor when constructing vehicle-based bathymetric maps. This thesis presents a methodology to produce self-consistent maps and simultaneously improve vehicle position estimation by exploiting accurate local navigation and utilizing terrain relative measurements. It is common for errors in the vehicle position estimate to far exceed the errors associated with the acoustic range sensor. This disparity creates inconsistency when an area is imaged multiple times and causes artifacts that distort map integrity. Our technique utilizes small terrain "submaps" that can be pairwise registered and used to additionally constrain the vehicle position estimates in accordance with actual bottom topography. A delayed state Kalman filter is used to incorporate these sub-map registrations as relative position measurements between previously visited vehicle locations. The archiving of previous positions in a filter state vector allows for continual adjustment of the sub-map locations. The terrain registration is accomplished using a two dimensional correlation and a six degree of freedom point cloud alignment method tailored for bathymetric data. The complete bathymetric map is then created from the union of all sub-maps that have been aligned in a consistent manner. Experimental results from the fully automated processing of a multibeam survey over the TAG hydrothermal structure at the Mid-Atlantic ridge are presented to validate the proposed method.This work was funded by the CenSSIS ERC of the Nation Science Foundation under grant EEC-9986821 and in part by the Woods Hole Oceanographic Institution through a grant from the Penzance Foundation

    Submeter bathymetric mapping of volcanic and hydrothermal features on the East Pacific Rise crest at 9°50′N

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    Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry Geophysics Geosystems 8 (2007): Q01006, doi:10.1029/2006GC001333.Recent advances in underwater vehicle navigation and sonar technology now permit detailed mapping of complex seafloor bathymetry found at mid-ocean ridge crests. Imagenex 881 (675 kHz) scanning sonar data collected during low-altitude (~5 m) surveys conducted with DSV Alvin were used to produce submeter resolution bathymetric maps of five hydrothermal vent areas at the East Pacific Rise (EPR) Ridge2000 Integrated Study Site (9°50′N, “bull's-eye”). Data were collected during 29 dives in 2004 and 2005 and were merged through a grid rectification technique to create high-resolution (0.5 m grid) composite maps. These are the first submeter bathymetric maps generated with a scanning sonar mounted on Alvin. The composite maps can be used to quantify the dimensions of meter-scale volcanic and hydrothermal features within the EPR axial summit trough (AST) including hydrothermal vent structures, lava pillars, collapse areas, the trough walls, and primary volcanic fissures. Existing Autonomous Benthic Explorer (ABE) bathymetry data (675 kHz scanning sonar) collected at this site provide the broader geologic context necessary to interpret the meter-scale features resolved in the composite maps. The grid rectification technique we employed can be used to optimize vehicle time by permitting the creation of high-resolution bathymetry maps from data collected during multiple, coordinated, short-duration surveys after primary dive objectives are met. This method can also be used to colocate future near-bottom sonar data sets within the high-resolution composite maps, enabling quantification of bathymetric changes associated with active volcanic, hydrothermal and tectonic processes.This work was supported by an NSF Ridge2000 fellowship to V.L.F. and a Woods Hole Oceanographic Institution fellowship supported by the W. Alan Clark Senior Scientist Chair (D.J.F.). Funding was also provided by the Censsis Engineering Research Center of the National Science Foundation under grant EEC-9986821. Support for field and laboratory studies was provided by the National Science Foundation under grants OCE-9819261 (D.J.F. and M.T.), OCE-0096468 (D.J.F. and T.S.), OCE-0328117 (SMC), OCE-0525863 (D.J.F. and S.A.S.), OCE-0112737 ATM-0427220 (L.L.W.), and OCE- 0327261 and OCE-0328117 (T.S.). Additional support was provided by The Edwin Link Foundation (J.C.K.)

    Sensor Fusion of Structure-from-Motion, Bathymetric 3D, and Beacon-Based Navigation Modalities

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    This paper describes an approach for the fusion of 30 data underwater obtained from multiple sensing modalities. In particular, we examine the combination of imagebased Structure-From-Motion (SFM) data with bathymetric data obtained using pencil-beam underwater sonar, in order to recover the shape of the seabed terrain. We also combine image-based egomotion estimation with acousticbased and inertial navigation data on board the underwater vehicle. We examine multiple types of fusion. When fusion is pe?$ormed at the data level, each modality is used to extract 30 information independently. The 30 representations are then aligned and compared. In this case, we use the bathymetric data as ground truth to measure the accuracy and drijl of the SFM approach. Similarly we use the navigation data as ground truth against which we measure the accuracy of the image-based ego-motion estimation. To our knowledge, this is the frst quantitative evaluation of image-based SFM and egomotion accuracy in a large-scale outdoor environment. Fusion at the signal level uses the raw signals from multiple sensors to produce a single coherent 30 representation which takes optimal advantage of the sensors' complementary strengths. In this papel; we examine how lowresolution bathymetric data can be used to seed the higherresolution SFM algorithm, improving convergence rates, and reducing drift error. Similarly, acoustic-based and inertial navigation data improves the convergence and driji properties of egomotion estimation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86044/1/hsingh-35.pd

    Data-driven Loop Closure Detection in Bathymetric Point Clouds for Underwater SLAM

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    Simultaneous localization and mapping (SLAM) frameworks for autonomous navigation rely on robust data association to identify loop closures for back-end trajectory optimization. In the case of autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES), data association is particularly challenging due to the scarcity of identifiable landmarks in the seabed, the large drift in dead-reckoning navigation estimates to which AUVs are prone and the low resolution characteristic of MBES data. Deep learning solutions to loop closure detection have shown excellent performance on data from more structured environments. However, their transfer to the seabed domain is not immediate and efforts to port them are hindered by the lack of bathymetric datasets. Thus, in this paper we propose a neural network architecture aimed to showcase the potential of adapting such techniques to correspondence matching in bathymetric data. We train our framework on real bathymetry from an AUV mission and evaluate its performance on the tasks of loop closure detection and coarse point cloud alignment. Finally, we show its potential against a more traditional method and release both its implementation and the dataset used

    Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping

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    This thesis investigates efficient forms of Simultaneous Localization and Mapping (SLAM) that do not require explicit identification, tracking or association of map features. The specific application considered here is subsea robotic bathymetric mapping. In this context, SLAM allows a GPS-denied robot operating near the sea floor to create a self-consistent bathymetric map. This is accomplished using a Rao-Blackwellized Particle Filter (RBPF) whereby each particle maintains a hypothesis of the current vehicle state and map that is efficiently maintained using Distributed Particle Mapping. Through particle weighting and resampling, successive observations of the seafloor structure are used to improve the estimated trajectory and resulting map by enforcing map self consistency. The main contributions of this thesis are two novel map representations, either of which can be paired with the RBPF to perform SLAM. The first is a grid-based 2D depth map that is efficiently stored by exploiting redundancies between different maps. The second is a trajectory map representation that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronises it to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions. For the grid map approach this is done by extracting the predictions stored in the observed cells. For the trajectory map approach predictions are instead generated from a local reconstruction of their map using Gaussian Process Regression. While the former allows for faster map access the latter requires less memory and fully exploits the spatial correlation in the environment, allowing predictions of seabed depth to be generated in areas that were not directly observed previously. In this case particle resampling therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. Both approaches are validated using multibeam sonar data collected from several missions of varying scale by a variety of different Unmanned Underwater Vehicles. These trials demonstrate how the corrections provided by both approaches improve the trajectory and map when compared to dead reckoning fused with Ultra Short Baseline or Long Baseline observations. Furthermore, results are compared with a pre-existing state of the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. Lastly the added capabilities of the trajectory map are validated using two different bathymetric datasets. These demonstrate how navigation and mapping corrections can still be achieved when only sparse bathymetry is available (e.g. from a four beam Doppler Velocity Log sensor) or in missions where map overlap is minimal or even non-existent

    A comparison of depth sounder positioning techniques for hydrographic/bathymetric surveys

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    Techniques used for Hydrographic surveys have significantly progressed from early Lead Line techniques to the utilisation of soundings for determining the depth of a submerged surface. To allow for the formation of the digital terrain model (DTM) of the submerged surface the XYZ position of a recorded sounding must be determined through remotely positioning a depth sounding device in order to achieve a relationship between each of the soundings. There are three common methods utilised in Hydrographic surveys to achieve this: GPS; Robotic Total Station; and GPS coupled with Tidal Height Datum methods. This dissertation provides an investigation of the use of different techniques of positioning a digital Echo sounder whilst undertaking Hydrographic or bathymetric surveys. The methodology used for this project was based on framework established by International Federation of Surveyors (FIG). Such framework and standards covers the planning, execution and management of Hydrographic surveys. The methodology of this research involved completing a survey of an exposed tidal surface using Robotic Total Station. This surface was used as the standard of comparison. Once it became submerged, three additional surveys were completed utilising a depth sounder coupled with Robotic Total Station, RTK GPS and the Tidal Plane. For quality assurance, an additional survey using Robotic Total Station techniques was completed once the surface became exposed for a second time to ensure that the differences found between the methods and the base surface were not affected by topography changes due to tidal influences. Each of these terrain models determined from the sounding surveys were then related to the original survey, and their relationships evaluated. Each of the methods utilising soundings created a representation of the submerged DTM surface; however, there is some uncertainty present over their height characteristics related to Australian Height Datum (AHD). Total Station methods provided the least difference from the base DTM model, with RTK GPS and Tidal/Water methods providing marginally greater difference

    Navigating Autonomous Underwater Vehicles

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    Mapping Complex Marine Environments with Autonomous Surface Craft

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    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
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