Research on the Influence of Calibration Image on Reprojection Error

Author(s):  
Chen Hong ◽  
Wang Daiqiang ◽  
Chen Yuqing
Keyword(s):  
Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1516 ◽  
Author(s):  
Francisco Troncoso-Pastoriza ◽  
Pablo Eguía-Oller ◽  
Rebeca Díaz-Redondo ◽  
Enrique Granada-Álvarez ◽  
Aitor Erkoreka

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.


Author(s):  
C. Altuntas

<p><strong>Abstract.</strong> Image based dense point cloud creation is easy and low-cost application for three dimensional digitization of small and large scale objects and surfaces. It is especially attractive method for cultural heritage documentation. Reprojection error on conjugate keypoints indicates accuracy of the model and keypoint localisation in this method. In addition, sequential registration of the images from large scale historical buildings creates big cumulative registration error. Thus, accuracy of the model should be increased with the control points or loop close imaging. The registration of point point cloud model into the georeference system is performed using control points. In this study historical Sultan Selim Mosque that was built in sixteen century by Great Architect Sinan was modelled via photogrammetric dense point cloud. The reprojection error and number of keypoints were evaluated for different base/length ratio. In addition, georeferencing accuracy was evaluated with many configuration of control points with loop and without loop closure imaging.</p>


2013 ◽  
pp. 112-124
Author(s):  
Graziano Chesi ◽  
Yeung Sam Hung

Triangulation is a fundamental problem in computer vision that consists of estimating the 3D position of a point of the scene from the estimates of its image projections on some cameras and from the estimates of the projection matrices of these cameras. This chapter addresses multiple view L2 triangulation, i.e. triangulation for vision systems with a generic number of cameras where the sought 3D point is searched by minimizing the L2 norm of the image reprojection error. The authors consider the standard case where estimates of all the image points are available (referring to such a case as certain triangulation), and consider also the case where some of such estimates are not available for example due to occlusions (referring to such a case as uncertain triangulation). In the latter case, it is supposed that the unknown image points belong to known regions such as line segments or ellipses. For these problems, the authors propose a unified methodology that exploits the fundamental matrices among the views and provides a candidate 3D point through the solution of a convex optimization problem based on linear matrix inequalities (LMIs). Moreover, the chapter provides a simple condition that allows one to immediately establish whether the found 3D point is optimal. Various examples with synthetic and real data illustrate the proposed technique, showing in particular that the obtained 3D point is almost always optimal in practice, and that its computational time is indeed small.


2019 ◽  
Vol 56 (2) ◽  
pp. 021204
Author(s):  
周单 Zhou Dan ◽  
董秀成 Dong Xiucheng ◽  
张帆 Zhang Fan ◽  
陈威 Chen Wei

2020 ◽  
Vol 8 (4) ◽  
pp. 376-388
Author(s):  
Mario Borrero ◽  
Luke R. Stroth

AbstractIn the past decade, archaeologists have increasingly made use of photogrammetry, the process of creating 3D models from photographs, in a variety of field and lab settings. We argue that we must, as a discipline, develop a consistent methodology to ensure that 3D models are held to a consistent standard, including not only photographic protocol but also the documentation of model accuracy using an agreed-upon measure. To help develop this discussion, we present our system for incorporating photogrammetry into the documentation of architecture. This technique was developed at the site of Nim Li Punit, Belize, in 2018. Excavating architecture involves documenting the pre-excavated building, liberating overburden, documenting all in situ construction (including wall fall, fill stones, and standing architecture), drawing consolidated architecture, and documenting the final state of the post-excavated buildings. The generation of 3D models greatly assisted in all facets of the excavation, documentation, analysis, and consolidation processes. To ensure that our models were accurate, we documented the reprojection error and final model horizontal distortion to assess the quality of the model. We suggest that documenting both forms of error should become standard practice in any discussion of archaeological applications of photogrammetry.


Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2837 ◽  
Author(s):  
Ali ◽  
Suominen ◽  
Gotchev ◽  
Morales

In this paper, we propose two novel methods for robot-world-hand–eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called 'hand–eye' and 'robot-world-hand–eye', respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.


Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 421 ◽  
Author(s):  
Gwon An ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Kugjin Yun ◽  
Won-Sik Cheong ◽  
...  

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.


Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7107
Author(s):  
Livio Bisogni ◽  
Ramtin Mollaiyan ◽  
Matteo Pettinari ◽  
Paolo Neri ◽  
Marco Gabiccini

Rotary tables are often used to speed up the acquisition time during the 3D scanning of complex geometries. In order to avoid manual registration of the point clouds acquired with different orientations, automatic algorithms to compensate the rotation were developed. Alternatively, a proper calibration of the rotary axis with respect to the camera system is needed. Several methods are available in the literature, but they only consider a single-axis calibration. In this paper, a method for the simultaneous calibration of both axes of the table is proposed. A checkerboard is attached to the table, and several images with different poses are acquired. An optimization algorithm is then setup to determine the orientation and the locations of the two axes. A metric to assess the calibration quality was also defined by computing the average mean reprojection error. This metric is used to investigate the optimal number and distribution of the calibration poses, demonstrating that the optimum calibration results are achieved when a wider dispersion of the calibration poses is adopted.


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