Improved vehicle based multibeam bathymetry using sub-maps and SLAM

Roman, Christopher; Singh, Hanumant

doi:10.1109/iros.2005.1545340

Cited by 76 publications

(56 citation statements)

References 14 publications

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“…It is more common that an a priori map is not available-either the mission is to explore a new area, or to measure and characterize changes in the terrain. In this case, the vehicle might generate incremental maps from sensor data on the fly (e.g., [10,36]) or use simultaneous localization and mapping (SLAM) techniques (e.g., [37][38][39]). …”

Section: Terrain-relative Navigationmentioning

confidence: 99%

“…The terrain may be characterized by sonar range measurements to the seafloor [10,32,33,36,[38][39][40][41] or photographs of the seafloor [42][43][44][45]. For some missions, AUVs have navigated relative to sonar maps of moving targets like icebergs [46,47].…”

Section: Terrain-relative Navigationmentioning

confidence: 99%

“…Long baseline (LBL), short baseline (SBL), or ultra-short baseline (USBL) acoustic positioning systems provide measurements in the NorthEast plane. More recently, terrain-relative techniques using bathymetry and/or photography have been developed to provide relative position measurements [38,45,90] for navigation aiding.…”

Section: Delayed Measurements In Underwater Positioningmentioning

confidence: 99%

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Contributions to automated realtime underwater navigation

Stanway¹

2012

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This dissertation presents three separate-but related-contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field-some have been demonstrated in all three arenas.The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors.The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current-the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. 3Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework-without altering the filter structure or requiring any ad-hoc adjust...

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Section: Terrain-relative Navigationmentioning

confidence: 99%

Section: Terrain-relative Navigationmentioning

confidence: 99%

Section: Delayed Measurements In Underwater Positioningmentioning

confidence: 99%

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Contributions to automated realtime underwater navigation

Stanway¹

2012

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“…Research in the marine domain has primarily focused on bathymetric mapping and mine counter-measures with AUVs and ROVs [12,13,14,24]. Typical AUV and ROV operations are performed in oceans with sufficient depth for launching the vehicle from a ship.…”

Section: Introductionmentioning

confidence: 99%

Mapping Complex Marine Environments with Autonomous Surface Craft

Leedekerken

Fallon

Leonard

2014

Experimental Robotics

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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.

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“…Some introduce artificial beacons to deal with complex environments Williams, Newman, Rosenblatt, Dissanayake, & Durrant-Whyte, 2001). An alternative approach is analyzing the three-dimensional (3D) structure of the environment to perform SLAM, either using bathymetric scans (Roman & Singh, 2005) or producing a tessellated representation of the scenario (Fairfield, Jonak, Kantor, & Wettergreen, 2007). The use of cameras is limited to applications in which the vehicle navigates in clear water and very near to the seafloor (Eustice, Singh, Leonard, Walter, & Ballard, 2005).…”

Section: Introductionmentioning

confidence: 99%

Underwater SLAM in man‐made structured environments

Ribas

Ridao

Tardós

et al. 2008

Journal of Field Robotics

167

115

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This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas, and marine platforms. A mechanically scanned imaging sonar is used to obtain information about the location of vertical planar structures present in such environments. A robust voting algorithm has been developed to extract line features, together with their uncertainty, from the continuous sonar data flow. The obtained information is incorporated into a feature-based simultaneous localization and mapping (SLAM) algorithm running an extended Kalman filter. Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Moreover, a procedure to build and maintain a sequence of local maps and to posteriorly recover the full global map has been adapted for the application presented. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach. C 2008 Wiley Periodicals, Inc.

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Improved vehicle based multibeam bathymetry using sub-maps and SLAM

Cited by 76 publications

References 14 publications

Contributions to automated realtime underwater navigation

Contributions to automated realtime underwater navigation

Mapping Complex Marine Environments with Autonomous Surface Craft

Underwater SLAM in man‐made structured environments

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