Automatic Processing of High-Rate, High-Density Multibeam Echosounder Data

Multibeam echosounders (MBES) are currently the best way to determine the bathymetry of large regions of the seabed with high accuracy. They are becoming the standard instrument for hydrographic surveying and are also used in geological studies, mineral exploration and scientific investigation of the earth\u27s crustal deformations and life cycle. The significantly increased data density provided by an MBES has significant advantages in accurately delineating the morphology of the seabed, but comes with the attendant disadvantage of having to handle and process a much greater volume of data. Current data processing approaches typically involve (computer aided) human inspection of all data, with time-consuming and subjective assessment of all data points. As data rates increase with each new generation of instrument and required turn-around times decrease, manual approaches become unwieldy and automatic methods of processing essential. We propose a new method for automatically processing MBES data that attempts to address concerns of efficiency, objectivity, robustness and accuracy. The method attributes each sounding with an estimate of vertical and horizontal error, and then uses a model of information propagation to transfer information about the depth from each sounding to its local neighborhood. Embedded in the survey area are estimation nodes that aim to determine the true depth at an absolutely defined location, along with its associated uncertainty. As soon as soundings are made available, the nodes independently assimilate propagated information to form depth hypotheses which are then tracked and updated on-line as more data is gathered. Consequently, we can extract at any time a “current-best” estimate for all nodes, plus co-located uncertainties and other metrics. The method can assimilate data from multiple surveys, multiple instruments or repeated passes of the same instrument in real-time as data is being gathered. The data assimilation scheme is sufficiently robust to deal with typical survey echosounder errors. Robustness is improved by pre-conditioning the data, and allowing the depth model to be incrementally defined. A model monitoring scheme ensures that inconsistent data are maintained as separate but internally consistent depth hypotheses. A disambiguation of these competing hypotheses is only carried out when required by the user. The algorithm has a low memory footprint, runs faster than data can currently be gathered, and is suitable for real-time use. We call this algorithm CUBE (Combined Uncertainty and Bathymetry Estimator). We illustrate CUBE on two data sets gathered in shallow water with different instruments and for different purposes. We show that the algorithm is robust to even gross failure modes, and reliably processes the vast majority of the data. In both cases, we confirm that the estimates made by CUBE are statistically similar to those generated by hand

A Time Comparison of Computer-Assisted and Manual Bathymetric Processing

We describe an experiment designed to determine the time required to process Multibeam Echosounder (MBES) data using the CUBE (Combined Uncertainty and Bathymetry Estimator) [Calder & Mayer, 2003; Calder, 2003] and Navigation Surface [Smith et al., 2002; Smith, 2003] algorithms. We collected data for a small (22.3xl06 soundings) survey in Valdez Narrows, Alaska, and monitored person-hours expended on processing for a traditional MBES processing stream and the proposed computer-assisted method operating on identical data. The analysis shows that the vast majority of time expended in a traditional processing stream is in subjective hand-editing of data, followed by line planning and quality control, and that the computer-assisted method is significantly faster than the traditional process through its elimination of human interaction time. The potential improvement in editing time is shown to be on the order of 25-37:1 over traditional methods

Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping

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

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

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

GeoZui3D: Data Fusion for Interpreting Oceanographic Data

GeoZui3D stands for Geographic Zooming User Interface. It is a new visualization software system designed for interpreting multiple sources of 3D data. The system supports gridded terrain models, triangular meshes, curtain plots, and a number of other display objects. A novel center of workspace interaction method unifies a number of aspects of the interface. It creates a simple viewpoint control method, it helps link multiple views, and is ideal for stereoscopic viewing. GeoZui3D has a number of features to support real-time input. Through a CORBA interface external entities can influence the position and state of objects in the display. Extra windows can be attached to moving objects allowing for their position and data to be monitored. We describe the application of this system for heterogeneous data fusion, for multibeam QC and for ROV/AUV monitoring

Self consistent bathymetric mapping from robotic vehicles in the deep ocean

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

Investigating tectonic and bathymetric features of the Indian Ocean using MAGSAT magnetic anomaly data

MAGSAT Investigator-B tapes were preprocessed by (1) removing all data points with obvious erroneous values and location errors; (2) removing smaller spikes (typically 15 nT or more), and deleting data tracks with fewer than 20 points; and (3) removing a linear trend from each track. The remaining data were recorded on tape for use by the equivalent source mapping (ESMAP) program which uses a least squares algorithm to fit the magnetization parameter of the grid of equivalent source dipoles in the crust to satellite data acquired at different times and locations. ESMAP was implemented on the TASC computing system and modified to read preprocessed MAGSAT tapes and interface with TASC plotting software. Some verification of the software was accomplished. Gridded 1-degree mean values of gravity anomaly and sea surface undulation computed from SEASAT radar altimeter were obtained and brought on line

An Autonomous Surface Vehicle for Long Term Operations

Environmental monitoring of marine environments presents several challenges: the harshness of the environment, the often remote location, and most importantly, the vast area it covers. Manual operations are time consuming, often dangerous, and labor intensive. Operations from oceanographic vessels are costly and limited to open seas and generally deeper bodies of water. In addition, with lake, river, and ocean shoreline being a finite resource, waterfront property presents an ever increasing valued commodity, requiring exploration and continued monitoring of remote waterways. In order to efficiently explore and monitor currently known marine environments as well as reach and explore remote areas of interest, we present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient power and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design and a discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto