Opti-Acoustic Stereo Imaging: On System Calibration and 3-D Target Reconstruction

Negahdaripour, S.; Sekkati, Hicham; Pirsiavash, Hamed

doi:10.1109/tip.2009.2013081

Cited by 67 publications

(29 citation statements)

References 19 publications

(23 reference statements)

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“…Sekkati et al used extracted corner features from DIDSON frames to estimate vehicle motion over several frames [67]. In related work Negahdaripour et al combined the DIDSON with an optical camera for 3-D target reconstruction using opti-acoustic stereo [56]. Eustice et al [16] used constraints from overlapping camera frames within a SLAM information filter to estimate vehicle pose.…”

Section: Loop Closure: Ship Hull Inspectionmentioning

confidence: 99%

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Simultaneous Localization and Mapping in Marine Environments

Fallon

Johannsson

Kaess

et al. 2012

Marine Robot Autonomy

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Accurate navigation is a fundamental requirement for robotic systemsmarine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely low drift rates, the localization accuracy decays monotonically with distance traveled. Other approaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well as its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes

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Section: Loop Closure: Ship Hull Inspectionmentioning

confidence: 99%

“…Following the formulation in [55,56,67], we define the geometry of the imaging sonar and derive a model that describes how the image is formed. To generate an Mathematically, the imaging process can be described as follows.…”

Section: Imaging Sonar Geometrymentioning

confidence: 99%

Simultaneous Localization and Mapping in Marine Environments

Fallon

Johannsson

Kaess

et al. 2012

Marine Robot Autonomy

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“…What little research has been done on surface reconstruc tion by marine robots deals exclusively with underwater sur faces [17], [18], [8], [12], [14]. Currently there is no sufficient work done on surface reconstruction of both above-and below-water parts of marine structures.…”

Section: B Previous Workmentioning

confidence: 99%

3D-surface reconstruction for partially submerged marine structures using an Autonomous Surface Vehicle

Παπαδόπουλος

Kurniawati

Shariff

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Ahstract-Over the last eight years, significant scientific effort has been dedicated on the problem of 3-D surface reconstruction for structural systems. However, the critical area of marine structures remains insufficiently studied. The research presented here focuses on the problem of 3-D surface reconstruction in the marine environment. This work is an extension of our previous approach, in which a surface vehicle that was equipped with a powerful laser scanner was designed and used to scan the above-water part of the marine structure of interest. Here we propose the design of a novel surface vehicle that is capable of using laser scanners and a side-looking sonar to scan marine structures both above and below the waterline.We also study the issue of downsampling the dataset in order to perform efficient surface reconstruction of the considered 3-D geometry, and we present a methodology for combining and integrating data from the above-and below-water parts of the structure. To illustrate the proposed robotic platform and validate our algorithms, we present results from a set of experiments in the Singapore Sea. Specifically, we present 2 different maps: the above-water map and the combined above and below-water map. In both cases, we have two different maps: a lower quality map, that can be generated on-line, and a higher quality map that is generated off-line. To the best of our knowledge, our work is the only one that provides a 3-D model for both above-and below-water parts of marine structures. In this work we assumed a GPS-denied environment, without using any other navigation sensor such as DVL or INS.

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“…Given the limitations of working in the field and the lack of a suitably large tank, the approach presented in this paper does not rely on an explicit relative sensor calibration, but instead derives the relationship as a side-effect of the smoothing and mapping algorithm. Further examples of tight integration between cameras and acoustic sensors are presented in [Negahdaripour, 2007] and [Negahdaripour et al, 2009], which are concerned with local area imaging, rather than with the creation of large area maps.…”

Section: Introductionmentioning

confidence: 99%

Map Building Fusing Acoustic and Visual Information using Autonomous Underwater Vehicles

Kunz

Singh

2013

Journal of Field Robotics

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We present a system for automatically building 3-D maps of underwater terrain fusing visual data from a single camera with range data from multibeam sonar. The six-degree of freedom location of the camera relative to the navigation frame is derived as part of the mapping process, as are the attitude offsets of the multibeam head and the on-board velocity sensor. The system uses pose graph optimization and the square root information smoothing and mapping framework to simultaneously solve for the robot's trajectory, the map, and the camera location in the robot's frame. Matched visual features are treated within the pose graph as images of 3-D landmarks, while multibeam bathymetry submap matches are used to impose relative pose constraints linking robot poses from distinct tracklines of the dive trajectory. The navigation and mapping system presented works under a variety of deployment scenarios, on robots with diverse sensor suites. Results of using the system to map the structure and appearance of a section of coral reef are presented using data acquired by the Seabed autonomous underwater vehicle.

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Opti-Acoustic Stereo Imaging: On System Calibration and 3-D Target Reconstruction

Cited by 67 publications

References 19 publications

Simultaneous Localization and Mapping in Marine Environments

Simultaneous Localization and Mapping in Marine Environments

3D-surface reconstruction for partially submerged marine structures using an Autonomous Surface Vehicle

Map Building Fusing Acoustic and Visual Information using Autonomous Underwater Vehicles

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