Autonomous underwater simultaneous localisation and map building

Williams, Stefan B.; Newman, Paul; Dissanayake, Gamini; Durrant‐Whyte, Hugh

doi:10.1109/robot.2000.844855

Cited by 85 publications

(54 citation statements)

References 6 publications

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“…(Williams et al, 2000), (Hernàndez et al, 2009), and (Fairfield et al, 2006)). The work of Thurn (Thrun et al, 2005) includes a good survey of the core techniques capable of fusing data from multiple sensors to create maps.…”

Section: Mapping Via Underwater Robot Systemsmentioning

confidence: 99%

Uncertainty Visualization and Hole Filling for Geometric Models of Ancient Water Systems

Forrester¹,

McVicker²,

Gambin³

et al. 2013

Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Inform

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Abstract:Geometric data acquired via a scanning process can suffer from holes due to errors in the acquisition process, noise, or challenges in merging multiple inputs together into a unified map. We present a straight forward algorithm to fill holes in incomplete evidence grids representing acquired geometric data. We also present our methods to apply learning in order to statistically evaluate the proposed hole filling algorithm. This analysis validates our proposed method for hole filling and additionally enables the construction of a probability distribution function to represent the accuracy of the filled data per model. During surface reconstruction, this function can be used to visualize the certainty of the filled geometry via transparency and coloring giving the user an understanding of the data's accuracy. This work is motivated by a multi-year project to construct educational visualizations of ancient water storage systems, i.e. cisterns and wells within churches, fortresses and homes on the islands of Malta, Gozo and Sicily.

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Section: Mapping Via Underwater Robot Systemsmentioning

confidence: 99%

Uncertainty Visualization and Hole Filling for Geometric Models of Ancient Water Systems

Forrester¹,

McVicker²,

Gambin³

et al. 2013

Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Inform

View full text Add to dashboard Cite

show abstract

“…There has been work on off-line SLAM methods using tunnel cross-sections, or slide im-ages, which can be derived from sparse sonar ranges as long as the environment is tunnel-shaped (Bradley et al 2004). 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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“…Williams and Newman [4] proposed autonomous underwater simultaneous localization and map building based on the sonar. Kim [5] and Langelaan [6] applied the SLAM to the UAV(Unmanned Aerial Vehicle).…”

Section: Related Researchmentioning

confidence: 99%

Research on Dynamic Simulation of Underwater Vehicle Manipulator Systems

Zhang

Wang

2008

OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean

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Abstract-This paper presents a simulator to simulating two key problems-underwater perception and autonomous manipulation。In the simulator a controller-observer strategy is proposed. A new SLAM (Simultaneous Localization and Mapping) algorithm-UKF (Unscented Kalman Filter) is used to estimate the state of UVMS (Underwater Vehicle Manipulator Systems) and the target object. 3D Simulation results show that the new SLAM algorithm can get the optimal estimation of state of UVMS and the target object, and the end-effector of manipulator can reach the desired object and the accuracy is centimeter level.

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Autonomous underwater simultaneous localisation and map building

Cited by 85 publications

References 6 publications

Uncertainty Visualization and Hole Filling for Geometric Models of Ancient Water Systems

Uncertainty Visualization and Hole Filling for Geometric Models of Ancient Water Systems

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

Research on Dynamic Simulation of Underwater Vehicle Manipulator Systems

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