Submap bathymetric SLAM using structured light in underwater environments

Underwater inspection, maintenance and repair (IMR) operations are being increasingly robotized in order to reduce safety issues and costs. These robotic systems rely on vision sensors to perform fundamental tasks, such as navigation and object recognition and manipulation. Especially, active optical 3D scanners are commonly used due to the domain-specific challenges of underwater imaging. This paper presents an exhaustive survey on the state of the art of optical 3D underwater scanners. A literature review on light projection and light-sensing technologies is presented. Moreover, quantitative performance comparisons of underwater 3D scanners present in the literature and commercial products are carried out.

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“…Laser detection was improved by using machine learning techniques. Then, a similar system was used to do SLAM [144].…”

Section: Quantitative Analysis Of Current Technologiesmentioning

confidence: 99%

State of the Art of Underwater Active Optical 3D Scanners

Castillón

Palomer

Forest

et al. 2019

Sensors

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“…If the environment is texture-less, an alternative is to use laser-camera systems, where the laser produces the necessary texture to extract point clouds from the environment. Initial developments of this approach relied on a fixed laser scanner that, combined with the vehicle motion, produces the point clouds [28,29], but suffers from navigation drift.…”

Section: Underwater Slammentioning

confidence: 99%

Semantic Mapping for Autonomous Subsea Intervention

Vallicrosa

Himri

Ridao

et al. 2021

Sensors

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This paper presents a method to build a semantic map to assist an underwater vehicle-manipulator system in performing intervention tasks autonomously in a submerged man-made pipe structure. The method is based on the integration of feature-based slam and 3D object recognition using a database of a priori known objects. The robot uses dvl, pressure, and ahrs sensors for navigation and is equipped with a laser scanner providing non-coloured 3D point clouds of the inspected structure in real time. The object recognition module recognises the pipes and objects within the scan and passes them to the slam, which adds them to the map if not yet observed. Otherwise, it uses them to correct the map and the robot navigation if they were already mapped. The slam provides a consistent map and a drift-less navigation. Moreover, it provides a global identifier for every observed object instance and its pipe connectivity. This information is fed back to the object recognition module, where it is used to estimate the object classes using Bayesian techniques over the set of those object classes which are compatible in terms of pipe connectivity. This allows fusing of all the already available object observations to improve recognition. The outcome of the process is a semantic map made of pipes connected through valves, elbows and tees conforming to the real structure. Knowing the class and the position of objects will enable high-level manipulation commands in the near future.

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“…When the robot works in an area where there is not too much texture, the alternative is to use lasercamera systems. The literature presents works where these systems are used to produce seabed maps in a SLAM framework (Inglis, Smart, Vaughn, & Roman, 2012;Massot-Campos, Oliver, Bodenmann, & Thornton, 2016). In these two works, the systems gather the 3D information by accumulating 2.5D laser profiles combined with the vehicle motion, in a similar way to a multibeam echo-sounder.…”

Section: Related Workmentioning

confidence: 99%

Inspection of an underwater structure using point‐cloud SLAM with an AUV and a laser scanner

Palomer

Ridao

Ribas³

2019

Journal of Field Robotics

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This paper presents experimental results using a newly developed 3D underwater laser scanner mounted on an autonomous underwater vehicle (AUV) for real‐time simultaneous localization and mapping (SLAM). The algorithm consists of registering point clouds using a dual step procedure. First, a feature‐based coarse alignment is performed, which is then refined using iterative closest point. The robot position is estimated using an extended Kalman filter (EKF) that fuses the data coming from navigation sensors of the AUV. Moreover, the pose from where each point cloud was collected is also stored in the pose‐based EKF‐SLAM state vector. The results of the registration algorithm are used as constraint observations among the different poses within the state vector, solving the full‐SLAM problem. The method is demonstrated using the Girona 500 AUV, equipped with a laser scanner and inspecting a 3D sub‐sea infrastructure inside a water tank. Our results prove that it is possible to limit the navigation drift and deliver a consistent high‐accuracy 3D map of the inspected object.

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Submap bathymetric SLAM using structured light in underwater environments

Cited by 17 publications

References 15 publications

State of the Art of Underwater Active Optical 3D Scanners

State of the Art of Underwater Active Optical 3D Scanners

Semantic Mapping for Autonomous Subsea Intervention

Inspection of an underwater structure using point‐cloud SLAM with an AUV and a laser scanner

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