Simultaneous localisation and mapping on the Great Barrier Reef

Williams, Stefan B.; Mahon, Ian

doi:10.1109/robot.2004.1308080

Cited by 97 publications

(48 citation statements)

References 7 publications

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“…In the case where there are free floating artificial features, scanning sonars have been shown to have high enough resolution to support feature-based SLAM (Williams et al 2000). Alternatively, in clear water with good lighting, SLAM has been demonstrated via video mosaicing (Eustice et al 2005) and also a combination of vision-based feature detection and sonar (Williams and Mahon 2004).…”

Section: Related Workmentioning

confidence: 99%

Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

Fairfield

Kantor

Wettergreen

2007

Journal of Field Robotics

187

110

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At 110m in diameter and over 350m in depth, the cenote Zacatón in central Mexico is a unique flooded sinkhole.A platform for conducting preliminary sonar tests is tethered in place. AbstractWe describe a Simultaneous Localization and Mapping (SLAM) method for a hovering underwater vehicle that will explore underwater caves and tunnels, a true three dimensional (3D) environment. Our method consists of a Rao-Blackwellized particle filter with a 3D evidence grid map representation. We describe a procedure for dynamically adjusting the number of particles to provide real-time performance. We also describe how we adjust the particle filter prediction step to accommodate sensor degradation or failure. We present an efficient octree data structure which makes it feasible to maintain the hundreds of maps needed by the particle filter to accurately model large environments. This octree structure can exploit spatial locality and temporal shared ancestry between particles to reduce the processing and storage requirements. To test our SLAM method, we utilize data collected with manually-deployed sonar mapping vehicles in the Wakulla Springs cave system in Florida and the Sistema Zacatón in Mexico, as well as data collected by the DEPTHX vehicle in the test tank at the Austin Applied Research Laboratory. We demonstrate our mapping and localization approach with these realworld datasets.

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Section: Related Workmentioning

confidence: 99%

Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

Fairfield

Kantor

Wettergreen

2007

Journal of Field Robotics

187

110

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“…Williams et al describes a method of underwater SLAM that takes advantage of both sonar and visual information for feature extraction in reef environments (Williams & Mahon 2004). Unfortunately the performance of the system during testing is difficult to evaluate as no ground truth was available for comparison.…”

Section: Positioning and Localisationmentioning

confidence: 99%

Computer Vision Applications in the Navigation of Unmanned Underwater Vehicles

Horgan¹,

Toal²

2009

Underwater Vehicles

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“…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). On the other hand, the visual information can also be combined with acoustic data to improve the overall reliability of the SLAM system (Williams & Mahon, 2004).…”

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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Simultaneous localisation and mapping on the Great Barrier Reef

Cited by 97 publications

References 7 publications

Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

Real‐Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels

Computer Vision Applications in the Navigation of Unmanned Underwater Vehicles

Underwater SLAM in man‐made structured environments

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