A Laser Line Auto-Scanning System for Underwater 3D Reconstruction

Chi, Shaopeng; Xie, Zhihong; Chen, Wenzhu

doi:10.3390/s16091534

Cited by 56 publications

(38 citation statements)

References 32 publications

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“…Our conclusion is consistent with the study by Starr and Lattimer [1]. Some specialized subsea LiDAR systems (please refer to Massot-Campos and Oliver-Codina [2] for a comprehensive survey of underwater 3D reconstruction), laser line scanning [3] or structured light [4,5,6,7] are able to operate in scattering media. However, these systems are power-consuming, slow, and bulky and thus they are not well suited for a walking robot.…”

Section: Introductionsupporting

confidence: 91%

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Nguyen

Park

Cho

et al. 2017

Sensors

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In this paper, we propose a model-based scattering removal method for stereo vision for robot manipulation in indoor scattering media where the commonly used ranging sensors are unable to work. Stereo vision is an inherently ill-posed and challenging problem. It is even more difficult in the case of images of dense fog or dense steam scenes illuminated by active light sources. Images taken in such environments suffer attenuation of object radiance and scattering of the active light sources. To solve this problem, we first derive the imaging model for images taken in a dense scattering medium with a single active illumination close to the cameras. Based on this physical model, the non-uniform backscattering signal is efficiently removed. The descattered images are then utilized as the input images of stereo vision. The performance of the method is evaluated based on the quality of the depth map from stereo vision. We also demonstrate the effectiveness of the proposed method by carrying out the real robot manipulation task.

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Section: Introductionsupporting

confidence: 91%

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Nguyen

Park

Cho

et al. 2017

Sensors

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“…However, the calibration procedure is quite complex and the objective function has 11 independent unknown parameters to be optimized. Similarly, Chi [14] designed a laser line auto-scanning system using a galvanometer and proposed the calibration method, but the calibration accuracy is not good enough. One-mirror galvanometric laser scanner is more efficient than the two-mirror galvanometric laser scanner and will not cause image distortion, so it has broader prospect in industrial application.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Calibration of the Galvanometric Laser Scanning Three-Dimensional Measurement System

Yang

Wang

et al. 2018

Nanomanuf Metrol

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The traditional three-dimensional measurement system based on linear-structured light usually involves mechanical scanning platforms to perform the linear scanning, which make the structure of the system huge and complicated. The emergence of the galvanometric laser scanner solves the problem by using a galvanometer to replace the mechanical scanning platform. The employment of the galvanometer can improve the speed of scanning and simplify the structure of the system. However, there are few approaches available to calibrate this kind of system. In this paper, a high-precision calibration method is proposed to calibrate the galvanometric laser scanning three-dimensional measurement system. A precision motorized linear stage and a planar target are applied in this method. The planar target is used for the camera calibration based on Zhang's method and the precision motorized linear stage for the laser plane calibration. The validity and accuracy of this method are evaluated by scanning the standard component. The experiments conducted suggest that the proposed method is valid and accurate for the calibration of the galvanometric laser scanning system.

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“…Moreover, the acquisition time of a point cloud is long due to the vehicle motion. The literature also presents laser-camera systems where the laser is steered manually (Bleier & Nüchter, 2017), with a robotic arm (Prats, Fernandez, & Sanz, 2012), where the camera and the laser are mounted onto a rotation actuator (2G Robotics, 2018), or where the laser is scanned across the camera image (Chantler, Clark, & Umasuthan, 1997;Kocak, Caimi, Das, & Karson, 1999;Hildebrandt, Kerdels, Albiez, & Kirchner, 2008;Chi, Xie, & Chen, 2016).…”

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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A Laser Line Auto-Scanning System for Underwater 3D Reconstruction

Cited by 56 publications

References 32 publications

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Novel Descattering Approach for Stereo Vision in Dense Suspended Scatterer Environments

Modeling and Calibration of the Galvanometric Laser Scanning Three-Dimensional Measurement System

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

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