Correcting the Eccentricity Error of Projected Spherical Objects in Perspective Cameras

Projective transformation of spheres onto images produce ellipses, whose centers do not coincide with the projected center of the sphere. This results in an eccentricity error, which must be treated in high precision metrology. This article provides closed formulations for modeling this error in images to enable 3-dimensional (3D) reconstruction of the center of spherical objects. The article also provides a new direct robust method for detecting spherical pattern in point clouds. It was shown that the eccentricity error in an image has only one component in the direction of the major axis of the ellipse. It was also revealed that the eccentricity is zero if and only if the center of the projected sphere lies on the camera’s perspective center. The effectiveness of the robust sphere detection and the eccentricity error modeling method was evaluated on simulated point clouds of spheres and real-world images, respectively. It was observed that the proposed robust sphere fitting method outperformed the popular M-estimator sample consensus in terms of radius and center estimation accuracy by a factor of 13, and 14 on average, respectively. Using the proposed eccentricity adjustment, the estimated 3D center of the sphere using modeled eccentricity was superior to the unmodeled case. It was also observed that the accuracy of the estimated 3D center using modeled eccentricity continuously improved as the number of images increased, whereas the unmodeled eccentricity did not show improvements after eight image views. The results of the investigation show that: (i) the proposed method effectively modeled the eccentricity error, and (ii) the effects of eliminating the eccentricity error in the 3D reconstruction become even more pronounced in a larger number of image views.

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Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-04-2013-347 ◽

2014 ◽

Vol 41 (2) ◽

pp. 203-212 ◽

Cited By ~ 13

Author(s):

Suyong Yeon ◽

ChangHyun Jun ◽

Hyunga Choi ◽

Jaehyeon Kang ◽

Youngmok Yun ◽

...

Keyword(s):

Principal Component ◽

Point Clouds ◽

Previous Method ◽

Divide And Conquer ◽

Sensor Data ◽

Estimation Accuracy ◽

Content Type ◽

Extraction Algorithm ◽

Plane Extraction ◽

Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

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An Egg Volume Measurement System Based on the Microsoft Kinect

Sensors ◽

10.3390/s18082454 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2454 ◽

Cited By ~ 11

Author(s):

Ting Chan ◽

Derek Lichti ◽

Adam Jahraus ◽

Hooman Esfandiari ◽

Herve Lahamy ◽

...

Keyword(s):

Least Squares ◽

Point Cloud ◽

Least Squares Method ◽

Point Clouds ◽

Volume Estimation ◽

Microsoft Kinect ◽

Estimation Accuracy ◽

Shape Parameters ◽

Post Processing ◽

Egg Volume

Measuring the volume of bird eggs is a very important task for the poultry industry and ornithological research due to the high revenue generated by the industry. In this paper, we describe a prototype of a new metrological system comprising a 3D range camera, Microsoft Kinect (Version 2) and a point cloud post-processing algorithm for the estimation of the egg volume. The system calculates the egg volume directly from the egg shape parameters estimated from the least-squares method in which the point clouds of eggs captured by the Kinect are fitted to novel geometric models of an egg in a 3D space. Using the models, the shape parameters of an egg are estimated along with the egg’s position and orientation simultaneously under the least-squares criterion. Four sets of experiments were performed to verify the functionality and the performance of the system, while volumes estimated from the conventional water displacement method and the point cloud captured by a survey-grade laser scanner serve as references. The results suggest that the method is straightforward, feasible and reliable with an average egg volume estimation accuracy 93.3% when compared to the reference volumes. As a prototype, the software part of the system was implemented in a post-processing mode. However, as the proposed processing techniques is computationally efficient, the prototype can be readily transformed into a real-time egg volume system.

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

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Principal Component Analysis For ICP Pose Estimation Of Space Structures

10.32920/ryerson.14656941.v1 ◽

2021 ◽

Author(s):

Lun H. Mark

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Optimal Combination ◽

Estimation Accuracy ◽

Space Structures ◽

Complex Objects ◽

Error Norm ◽

Simulation Results

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OPTIMIZING RADIOMETRIC PROCESSING AND FEATURE EXTRACTION OF DRONE BASED HYPERSPECTRAL FRAME FORMAT IMAGERY FOR ESTIMATION OF YIELD QUANTITY AND QUALITY OF A GRASS SWARD

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1305-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1305-1310 ◽

Cited By ~ 5

Author(s):

R. Näsi ◽

N. Viljanen ◽

R. Oliveira ◽

J. Kaivosoja ◽

O. Niemeläinen ◽

...

Keyword(s):

Feature Extraction ◽

3D Structure ◽

Point Clouds ◽

Estimation Accuracy ◽

Spectral Features ◽

Data Set ◽

Area Of Interest ◽

Evaluation Task ◽

Corrected Data ◽

Grass Sward

Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10&thinsp;% better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40&thinsp;% compared to approaches where only spectral features were applied. The best estimation RMSE of 239&thinsp;kg/ha (6.0&thinsp;%) was obtained with multiview anisotropy corrected data set and the 3D features.

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A Robust Feature Points Matching Algorithm in 3D Optical Measuring System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.5193 ◽

2011 ◽

Vol 383-390 ◽

pp. 5193-5199 ◽

Cited By ~ 2

Author(s):

Jian Ying Yuan ◽

Xian Yong Liu ◽

Zhi Qiang Qiu

Keyword(s):

3D Reconstruction ◽

Digital Camera ◽

Image Data ◽

Epipolar Geometry ◽

Bundle Adjustment ◽

Measuring System ◽

Feature Points ◽

3 Dimensional ◽

Feature Points Matching ◽

Optical Measuring

In optical measuring system with a handheld digital camera, image points matching is very important for 3-dimensional(3D) reconstruction. The traditional matching algorithms are usually based on epipolar geometry or multi-base lines. Mistaken matching points can not be eliminated by epipolar geometry and many matching points will be lost by multi-base lines. In this paper, a robust algorithm is presented to eliminate mistaken matching feature points in the process of 3D reconstruction from multiple images. The algorithm include three steps: (1) pre-matching the feature points using constraints of epipolar geometry and image topological structure firstly; (2) eliminating the mistaken matching points by the principle of triangulation in multi-images; (3) refining camera external parameters by bundle adjustment. After the external parameters of every image refined, repeat step (1) to step (3) until all the feature points been matched. Comparative experiments with real image data have shown that mistaken matching feature points can be effectively eliminated, and nearly no matching points have been lost, which have a better performance than traditonal matching algorithms do.

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