Wide Angle Rigid Registration Using a Comparative Tensor Shape Factor

2017 ◽  
Vol 17 (01) ◽  
pp. 1750006 ◽  
Author(s):  
Luciano W. X. Cejnog ◽  
Fernando A. A. Yamada ◽  
Marcelo Bernardes Vieira
Keyword(s):  
Euclidean Distance ◽  
Additive Noise ◽  
Point Clouds ◽  
Local Geometry ◽  
Rigid Registration ◽  
Weight Variation ◽  
Orientation Tensors ◽  
Point Search ◽  
Proposed Modification ◽  
Made In

This work aims to enhance a classic method for rigid registration, the iterative closest point (ICP), modifying the closest point search in order to consider approximated information of local geometry combined to the Euclidean distance, originally used. For this, a preprocessing stage is applied, in which the local geometry is encoded in second-order orientation tensors. We define the CTSF, a similarity factor between tensors. Our method uses a strategy of weight variation between the CTSF and the Euclidean distance, in order to establish correspondences. Quantitative tests were made in point clouds with different geometric features, with variable levels of additive noise and outliers and in partial overlapping situations. Results show that the proposed modification increases the convergence probability of the method for higher angles, making the method comparable to state-of-art techniques.

scholarly journals A Shape-Based Weighting Strategy Applied to the Covariance Estimation on ICP

2017 ◽  
Vol 17 (04) ◽  
pp. 1750021
Author(s):  
F. A. A. Yamada ◽  
L. W. X. Cejnog ◽  
M. B. Vieira ◽  
R. L. S. da Silva
Keyword(s):  
Covariance Matrix ◽  
Angular Displacement ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Local Geometry ◽  
Rigid Registration ◽  
Common Solution ◽  
Cross Covariance ◽  
Heuristic Strategy ◽  
Orientation Tensors

In the pairwise rigid registration problem, we need to find a rigid transformation that aligns two point clouds. The classical and most common solution is the Iterative Closest Point (ICP) algorithm. However, the ICP and many of its variants require that the point clouds are already coarsely aligned. We present in this paper a method named Shape-based Weighting Covariance Iterative Closest Point (SWC-ICP) which improves the possibility to correctly align two point clouds, regardless of the initial pose, even when they are only partially overlapped, or in the presence of noise and outliers. It benefits from the local geometry of the points, encoded in second-order orientation tensors, to provide a second correspondences set to the ICP. The cross-covariance matrix computed from this set is combined with the usual cross-covariance matrix, following a heuristic strategy. In order to compare our method with some recent approaches, we present a detailed evaluation protocol to rigid registration. Results show that the SWC-ICP is among the best compared methods, with a better performance in situations of wide angular displacement of noisy point clouds.

scholarly journals Unmanned Aerial Vehicles (UAVs) for Physical Progress Monitoring of Construction

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4227
Author(s):  
Nicolás Jacob-Loyola ◽  
Felipe Muñoz-La Rivera ◽  
Rodrigo F. Herrera ◽  
Edison Atencio
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Progress Monitoring ◽  
Residential Building ◽  
Point Clouds ◽  
Construction Site ◽  
Manual Operation ◽  
Work Site ◽  
Aerial Vehicles ◽  
Made In

The physical progress of a construction project is monitored by an inspector responsible for verifying and backing up progress information, usually through site photography. Progress monitoring has improved, thanks to advances in image acquisition, computer vision, and the development of unmanned aerial vehicles (UAVs). However, no comprehensive and simple methodology exists to guide practitioners and facilitate the use of these methods. This research provides recommendations for the periodic recording of the physical progress of a construction site through the manual operation of UAVs and the use of point clouds obtained under photogrammetric techniques. The programmed progress is then compared with the actual progress made in a 4D BIM environment. This methodology was applied in the construction of a reinforced concrete residential building. The results showed the methodology is effective for UAV operation in the work site and the use of the photogrammetric visual records for the monitoring of the physical progress and the communication of the work performed to the project stakeholders.

scholarly journals Recognition of Point Sets Objects in Realistic Scenes

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ruizhen Gao ◽  
Xiaohui Li ◽  
Jingjun Zhang
Keyword(s):  
Network Structure ◽  
Point Cloud ◽  
Euclidean Distance ◽  
Three Dimensional ◽  
Point Clouds ◽  
Accuracy Rate ◽  
Cloud Data ◽  
3D Scanners ◽  
Deep Learning Network ◽  
Realistic Scenes

With the emergence of new intelligent sensing technologies such as 3D scanners and stereo vision, high-quality point clouds have become very convenient and lower cost. The research of 3D object recognition based on point clouds has also received widespread attention. Point clouds are an important type of geometric data structure. Because of its irregular format, many researchers convert this data into regular three-dimensional voxel grids or image collections. However, this can lead to unnecessary bulk of data and cause problems. In this paper, we consider the problem of recognizing objects in realistic senses. We first use Euclidean distance clustering method to segment objects in realistic scenes. Then we use a deep learning network structure to directly extract features of the point cloud data to recognize the objects. Theoretically, this network structure shows strong performance. In experiment, there is an accuracy rate of 98.8% on the training set, and the accuracy rate in the experimental test set can reach 89.7%. The experimental results show that the network structure in this paper can accurately identify and classify point cloud objects in realistic scenes and maintain a certain accuracy when the number of point clouds is small, which is very robust.

Rigid Registration of Point Clouds Based on Indirect Lie Group Approach

2019 ◽  
Author(s):  
Liliane Rodrigues de Almeida ◽  
Gilson Antonio Giraldi ◽  
Marcelo Bernardes Vieira
Keyword(s):  
Lie Group ◽  
Point Clouds ◽  
Group Approach ◽  
Rigid Registration

Rigid registration of noisy point clouds based on higher-dimensional error metrics

2018 ◽  
Vol 34 (6-8) ◽  
pp. 1021-1030 ◽  
Author(s):  
Enkhbayar Altantsetseg ◽  
Oyundolgor Khorloo ◽  
Kouichi Konno
Keyword(s):  
Point Clouds ◽  
Dimensional Error ◽  
Rigid Registration ◽  
Error Metrics ◽  
Higher Dimensional

Non-rigid registration of point clouds using landmarks and stochastic neighbor embedding

2021 ◽  
Vol 30 (03) ◽  
Author(s):  
Amar Maharjan ◽  
Xiaohui Yuan ◽  
Qiang Lu ◽  
Yuqi Fan ◽  
Tian Chen
Keyword(s):  
Point Clouds ◽  
Rigid Registration

scholarly journals Geodesic Distance on Gaussian Manifolds to Reduce the Statistical Errors in the Investigation of Complex Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Michele Lungaroni ◽  
Andrea Murari ◽  
Emmanuele Peluso ◽  
Pasqualino Gaudio ◽  
Michela Gelfusa
Keyword(s):  
Euclidean Distance ◽  
Additive Noise ◽  
Geodesic Distance ◽  
Type Ii ◽  
Additional Source ◽  
Numerical Tests ◽  
Important Improvement ◽  
Scientific Expertise ◽  
Type Ii Errors ◽  
Robust Statistical Methods

In the last years the reputation of medical, economic, and scientific expertise has been strongly damaged by a series of false predictions and contradictory studies. The lax application of statistical principles has certainly contributed to the uncertainty and loss of confidence in the sciences. Various assumptions, generally held as valid in statistical treatments, have proved their limits. In particular, since some time it has emerged quite clearly that even slightly departures from normality and homoscedasticity can affect significantly classic significance tests. Robust statistical methods have been developed, which can provide much more reliable estimates. On the other hand, they do not address an additional problem typical of the natural sciences, whose data are often the output of delicate measurements. The data can therefore not only be sampled from a nonnormal pdf but also be affected by significant levels of Gaussian additive noise of various amplitude. To tackle this additional source of uncertainty, in this paper it is shown how already developed robust statistical tools can be usefully complemented with the Geodesic Distance on Gaussian Manifolds. This metric is conceptually more appropriate and practically more effective, in handling noise of Gaussian distribution, than the traditional Euclidean distance. The results of a series of systematic numerical tests show the advantages of the proposed approach in all the main aspects of statistical inference, from measures of location and scale to size effects and hypothesis testing. Particularly relevant is the reduction even of 35% in Type II errors, proving the important improvement in power obtained by applying the methods proposed in the paper. It is worth emphasizing that the proposed approach provides a general framework, in which also noise of different statistical distributions can be dealt with.

scholarly journals Hue-assisted automatic registration of color point clouds

2014 ◽  
Vol 1 (4) ◽  
pp. 223-232 ◽  
Author(s):  
Hao Men ◽  
Kishore Pochiraju
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Local Geometry ◽  
Automatic Registration ◽  
Color Information ◽  
Registration Accuracy ◽  
Surface Color ◽  
Registration Algorithm ◽  
Sample Density ◽  
Occupancy Grids

Abstract This paper describes a variant of the extended Gaussian image based registration algorithm for point clouds with surface color information. The method correlates the distributions of surface normals for rotational alignment and grid occupancy for translational alignment with hue filters applied during the construction of surface normal histograms and occupancy grids. In this method, the size of the point cloud is reduced with a hue-based down sampling that is independent of the point sample density or local geometry. Experimental results show that use of the hue filters increases the registration speed and improves the registration accuracy. Coarse rigid transformations determined in this step enable fine alignment with dense, unfiltered point clouds or using Iterative Common Point (ICP) alignment techniques.

scholarly journals Rancang Bangun Sistem Presensi Menggunakan Sidik Jari (Studi Kasus Pada Lembaga Pemasyarakatan Klas II-A Palu)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Amriana Amriana ◽  
Andi Hendra ◽  
Arfiah Bakhtiar
Keyword(s):  
Euclidean Distance ◽  
Distance Measurement ◽  
Test Results ◽  
Image Binarization ◽  
Fingerprint Image ◽  
Unique Identity ◽  
Image Quality Improvement ◽  
Fingerprint Data ◽  
Made In

Dermatoglyphic has been widely applied to recognize a person's identity, because people basically have something unique or characteristic possessed only by themselves. This raises the idea of making it a unique identity. This is useful for employees presence applications, especially at Lembaga Pemasyarakatan Klas IIA Palu which is still doing manual presence. This system provides the division of labor time and wereable to detect the fingerprints of employees using a fingerprint scanner. The system was made in several stages of the input fingerprint image, binarization fingerprint image, fingerprint image quality improvement, and matching fingerprint image. Improvements to the quality of the fingerprint image consists of the surgical process of mathematical morphology opening and erosion. The detection process of the fingerprint image used euclidean distance measurement points minutiae. The test results indicate that the presence system accuracy using euclidean distance measurement could detect the fingerprint image by 89% from 20 fingerprint data with 5 times testing for each data.

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