3D reconstruction system based on incremental structure from motion using a camera with varying parameters

Hazzat, Soulaiman El; Merras, Mostafa; Akkad, Nabil El; Saaidi, Abderrahim; Satori, Khalid

doi:10.1007/s00371-017-1451-0

Cited by 24 publications

(6 citation statements)

References 33 publications

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“…The visual detection system is calibrated using various external and internal parameters. The internal parameters are the intrinsic parameters of the camera, which provide the conversion relationship between the camera and image coordinate systems [ 45 , 46 , 47 ]. The external parameters provide the relative position and attitude relationships between the camera and motion coordinate systems.…”

Section: Pose Measurement Principle For a Micro-mobile Robotmentioning

confidence: 99%

A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

et al. 2021

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This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving targets. The kernelized correlation filter algorithm then accurately detects the positions of the moving targets and uses the detection results to modify the moving states of the targets. This paper verifies the performance of the algorithm on a monocular vision measurement platform and using a pose measurement method. The K2CF algorithm was embedded in the micro-robot’s attitude measurement system, and the tracking performances of three different trackers were compared under different motion conditions. Our tracker improved the positioning accuracy and maintained real-time operation. In a comparison study of K2CF and many other algorithms on Object Tracking Benchmark-50 and Object Tracking Benchmark-100 video sequences, the K2CF algorithm achieved the highest accuracy. In the 400 mm × 300 mm field of view, when the target radius is about 3 mm and the inter-frame acceleration displacement does not exceed 5.6 mm, the root-mean-square error of position and attitude angle can satisfy the precision requirements of the system.

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Section: Pose Measurement Principle For a Micro-mobile Robotmentioning

confidence: 99%

A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

et al. 2021

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“…According to results of data denoising and data registration, 3D coordinate transformation of spatial structure is carried out. According to different functions, 3D coordinate transformation can be divided into two types: one is to convert laser scanning data into the form of 3D coordinates, and the other is to realize function of editing tools [18,19]. The data format collected by laser scanner is (L, θ b , φ, G).…”

Section: Coordinate Transformationmentioning

confidence: 99%

Virtual Reconstruction System of Building Spatial Structure Based on Laser 3D Scanning under Multivariate Big Data Fusion

Woźniak

2021

Mobile Netw Appl

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Existing systems have disadvantages such as slow running speed, long time-consuming, and poor rendering effect in virtual reconstruction of architectural spatial structure. In order to solve such problems, virtual reconstruction system of building space structure is designed using laser 3D scanning technology under condition of fusion of multiple big data. The system was equipped with a 3D laser scanner and connected to computer, and the noise interference was reduced by image preprocessing module to complete the hardware design. The system improved user interface and maintenance module. Eventually, 3D model reconstruction was realized via data acquisition, data registration, coordinate transformation and 3D rendering. The results show that the system designed in this paper runs fast, and color of reconstruction results is consistent, which indicates that reconstruction results of building space structure obtained by the system are conducive to in-depth study of building space.

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“…The progress in structure and motion estimation (a.k.a. structure-from-motion) research has been hectic, stimulated by recent breakthroughs in computer vision, the advent of digital photography and the augmented reality [1][2][3][4][5][6]. This progress has the potential to substantially increase the use of the structure from motion technique for a variety of applications, for example the growing application of unmanned aerial vehicles for remote surveying for a numerous of ecological domain [7].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional structure from motion recovery of a moving object with noisy measurement

Choi

2020

IJECE

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In this paper, a Nonlinear Unknown Input Observer (NLUIO) based approach is proposed for three-dimensional (3-D) structure from motion identification. Unlike the previous studies that require prior knowledge of either the motion parameters or scene geometry, the proposed approach assumes that the object motion is imperfectly known and considered as an unknown input to the perspective dynamical system. The reconstruction of the 3-D structure of the moving objects can be achieved using just two-dimensional (2-D) images of a monocular vision system. The proposed scheme is illustrated with a numerical example in the presence of measurement noise for both static and dynamic scenes. Those results are used to clearly demonstrate the advantages of the proposed NLUIO.

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3D reconstruction system based on incremental structure from motion using a camera with varying parameters

Cited by 24 publications

References 33 publications

A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

Virtual Reconstruction System of Building Spatial Structure Based on Laser 3D Scanning under Multivariate Big Data Fusion

Three-dimensional structure from motion recovery of a moving object with noisy measurement

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