KEYWORDS: Monte Carlo methods, Imaging systems, Distortion, Cameras, Visual process modeling, Optical engineering, Mathematical modeling, Systems modeling, Tolerancing, Statistical analysis
Due to the influence of many factors in monocular vision pose measurement, the reliability of pose measurement results needs to be evaluated by uncertainty. The difficulties of the traditional guide to the expression of uncertainty in measurement (GUM) method in actual pose evaluation are analyzed, and the adaptive Monte Carlo method (AMCM) is used to evaluate the uncertainty of pose results. According to the mathematical model of pose measurement, the factors affecting pose results are analyzed in detail. The uncertainty evaluation model is established by the error traceability method and the distribution of uncertainty components is analyzed reasonably. Both the Monte Carlo method (MCM) and AMCM are used to evaluate the uncertainty of pose results. The feasibility and effectiveness of the AMCM evaluation method are verified. The experimental results show that MCM and AMCM, as the supplement of the GUM method, can well solve the problem of pose evaluation in the actual measurement system. However, AMCM is more convenient and efficient than MCM to solve the problem of too large or too small sampling times.
KEYWORDS: Clouds, Reconstruction algorithms, Data modeling, Calibration, Data centers, 3D modeling, Spherical lenses, Reverse modeling, 3D metrology, Reverse engineering
The reconstruction of 3D point cloud is the core of reverse engineering and widely applied in industrial field. Focused on the problem of data redundancy and calculation, the reconstruction procedure is realized and the influence of various triangulation methods on geometric features is analyzed. The point cloud data pre-processing is implemented first based on C++ Point Cloud Library (PCL) in the paper, including filtering and smoothing, outlier removal, valid points extraction, simplification, and hole filling. Then the Greedy Projection Triangulation and the Poisson Reconstruction methods are applied separately to reconstruct the mesh models. The spherical center distance and diameter of calibration board are selected as the geometric characteristic parameters to assess the reconstruction quality. The relative error is calculated according to the true value and the average of multiple measurements on the parameters. For the distance feature, the results show that the two methods have similar accuracy. For the diameter feature, the Greedy Projection Triangulation is further suitable than the Poisson reconstruction, and the relative error of which is less than 0.18%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.