scholarly journals Flexible Three-Dimensional Reconstruction via Structured-Light-based Visual Positioning and Global Optimization

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1583 ◽  
Author(s):  
Lei Yin ◽  
Xiangjun Wang ◽  
Yubo Ni
Keyword(s):  
Global Optimization ◽  
Vision System ◽  
Structured Light ◽  
Three Dimensional ◽  
Iteration Algorithm ◽  
Localization Algorithm ◽  
Camera System ◽  
Precise Position ◽  
Visual Positioning ◽  
Structured Light Vision

Three-dimensional (3D) reconstruction using line structured light vision system commonly cooperates with motion restraint devices, such as parallel guide rail push-broom devices. In this study, we propose a visual positioning method to eliminate the motion constraint. An extended orthogonal iteration algorithm for visual positioning is proposed to obtain the precise position of the line structured light binocular camera system during movement. The algorithm uses the information acquired by the binocular camera, and produces a better positioning accuracy than the traditional vision localization algorithm. Furthermore, a global optimization method is proposed to calculate the poses of the camera relative to the world coordinate system at each shooting position. This algorithm effectively reduces the error accumulation and pose drift during visual positioning, and 3D information of the surface can be measured via the proposed free-moving line structured light vision system. The simulation and physical experiments performed herein validate the proposed method and demonstrate the significant improvement in the reconstruction accuracy: when the test distance is 1.5 m, the root mean square error of the point cloud is within 0.5 mm.

scholarly journals A Methodology for Multi-Camera Surface-Shape Estimation of Deformable Unknown Objects

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 69 ◽  
Author(s):  
Evgeny Nuger ◽  
Beno Benhabib
Keyword(s):  
Particle Filtering ◽  
Surface Deformation ◽  
Vision System ◽  
Three Dimensional ◽  
Target Object ◽  
Surface Shape ◽  
Shape Estimation ◽  
Formal Approach ◽  
Camera System ◽  
Unknown Objects

A novel methodology is proposed herein to estimate the three-dimensional (3D) surface shape of unknown, markerless deforming objects through a modular multi-camera vision system. The methodology is a generalized formal approach to shape estimation for a priori unknown objects. Accurate shape estimation is accomplished through a robust, adaptive particle filtering process. The estimation process yields a set of surface meshes representing the expected deformation of the target object. The methodology is based on the use of a multi-camera system, with a variable number of cameras, and range of object motions. The numerous simulations and experiments presented herein demonstrate the proposed methodology’s ability to accurately estimate the surface deformation of unknown objects, as well as its robustness to object loss under self-occlusion, and varying motion dynamics.

scholarly journals Multi-Camera and Structured-Light Vision System (MSVS) for Dynamic High-Accuracy 3D Measurements of Railway Tunnels

Sensors ◽  
2015 ◽  
Vol 15 (4) ◽  
pp. 8664-8684 ◽  
Author(s):  
Dong Zhan ◽  
Long Yu ◽  
Jian Xiao ◽  
Tanglong Chen
Keyword(s):  
Vision System ◽  
Structured Light ◽  
High Accuracy ◽  
3D Measurements ◽  
Structured Light Vision

scholarly journals Indoor Key Point Reconstruction Based on Laser Illumination and Omnidirectional Vision

2020 ◽  
Vol 24 (7) ◽  
pp. 864-871
Author(s):  
Yang Qi ◽  
◽  
Yuan Li
Keyword(s):  
Vision System ◽  
Structured Light ◽  
Three Dimensional ◽  
Mathematical Representation ◽  
Omnidirectional Vision ◽  
Wide Field ◽  
3D Vision ◽  
Imaging Model ◽  
Key Points ◽  
Indoor Scenes

Efficient and precise three-dimensional (3D) measurement is an important issue in the field of machine vision. In this paper, a measurement method for indoor key points is proposed with structured lights and omnidirectional vision system and the system can achieve the wide field of view and accurate results. In this paper, the process of obtaining indoor key points is as follows: Firstly, through the analysis of the system imaging model, an omnidirectional vision system based on structured light is constructed. Secondly, the full convolution neural network is used to estimate the scene for the dataset. Then, according to the geometric relationship between the scenery point and its reference point in structured light, for obtaining the 3D coordinates of the unstructured light point is presented. Finally, combining the full convolution network model and the structured light 3D vision model, the 3D mathematical representation of the key points of the indoor scene frame is completed. The experimental results proved that the proposed method can accurately reconstruct indoor scenes, and the measurement error is about 2%.

High-Frame-Rate Structured Light 3-D Vision for Fast Moving Objects

2014 ◽  
Vol 26 (3) ◽  
pp. 311-320 ◽  
Author(s):  
Yongjiu Liu ◽  
◽  
Hao Gao ◽  
Qingyi Gu ◽  
Tadayoshi Aoyama ◽  
...  
Keyword(s):  
Fast Moving ◽  
Moving Objects ◽  
Vision System ◽  
Structured Light ◽  
Gray Code ◽  
Frame Rate ◽  
3D Vision ◽  
High Frame Rate ◽  
Depth Images ◽  
Structured Light Vision

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00260003/04.jpg"" width=""300"" />HFR 3D vision system</span></div> This paper presents a fast motion-compensated structured-light vision system that realizes 3-D shape measurement at 500 fps using a high-frame-rate camera-projector system. Multiple light patterns with an 8-bit gray code, are projected on the measured scene at 1000 fps, and are processed in real time for generating 512 × 512 depth images at 500 fps by using the parallel processing of a motion-compensated structured-light method on a GPU board. Several experiments were performed on fast-moving 3-D objects using the proposed method. </span>

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