Scan matching SLAM in underwater environments

Mallios, Angelos; Ridao, Pere; Ribas, David; Hernández, Emili

doi:10.1007/s10514-013-9345-0

Cited by 72 publications

(59 citation statements)

References 37 publications

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“…During the survey, the monobeam rotating sonar head was positioned orthogonally to the cave, so that a single scan provides a 360° view of its walls. Additionally, in this case the trajectory was optimized a posteriori through a SLAM approach . Figure shows the data set along with two close‐up views allowing the understanding of the shape of the interior of the cave.…”

Section: Resultsmentioning

confidence: 99%

Surface meshing of underwater maps from highly defective point sets

Campos

García

2017

Journal of Field Robotics

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Owing to the many possible errors that may occur during real‐world mapping, point set maps often present a huge amount of outliers and large levels of noise. We present two robust surface reconstruction techniques dealing with corrupted point sets without resorting to any prefiltering step. They are based on building an unsigned distance function, discretely evaluated on an adaptive tetrahedral grid, and defined from an outlier‐robust splat representation. To extract the surface from this volumetric view, the space is partitioned into two subsets, the surface of interest being at the boundary separating them. While both methods are based on a similar graph definition derived from the above‐mentioned grid, they differ in the partitioning procedure. First, we propose a method using S‐T cuts to separate the inside and outside of the mapped area. Second, we use a normalized cut approach to partition the volume using only the values of the unsigned distance function. We prove the validity of our methods by applying them to challenging underwater data sets (sonar and image based), and we benchmark their results against the approaches in the state of the art.

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Section: Resultsmentioning

confidence: 99%

Surface meshing of underwater maps from highly defective point sets

Campos

García

2017

Journal of Field Robotics

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“…In our previous research (Mallios, Ridao, Ribas, & Hernández, ), we presented a pose‐based algorithm to solve the full SLAM problem of an AUV localization in a confined, unknown, and possibly unstructured environment. This algorithm does not rely on features or any structural information, which is potentially advantageous when applied in natural environments.…”

Section: Background and Closely Related Workmentioning

confidence: 99%

Toward Autonomous Exploration in Confined Underwater Environments

et al. 2015

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In this field note we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic sonar to simultaneously map the caves' horizontal and vertical surfaces. Although the caves' spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and map- * The author can be also reached at Computer Vision and Robotics Institute, Universitat de Girona, 17003, Girona, Spain, amallios@eia.udg.edu.ping (SLAM) framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or pre-deployment of localization equipment are not feasible and may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.

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“…They can explore the seafloor with no gaps in deep [53][54][55][56] or shallow water [57,58], to inspect submerged structures [59,60] or ship hulls [60,61], take samples in HABs [62], or perform fish monitoring [63] or pest population control [64,65]. AUV mission duration is usually constrained by their endurance.…”

Section: Biosecurity Applications Of Autonomous Surveillancementioning

confidence: 99%

Autonomous surveillance for biosecurity

Jurdak¹,

Elfes²,

Kusý³

et al. 2015

Trends in Biotechnology

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The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.

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Scan matching SLAM in underwater environments

Cited by 72 publications

References 37 publications

Surface meshing of underwater maps from highly defective point sets

Surface meshing of underwater maps from highly defective point sets

Toward Autonomous Exploration in Confined Underwater Environments

Autonomous surveillance for biosecurity

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