scholarly journals Multiple Cylinder Extraction from Organized Point Clouds

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7630
Author(s):  
Saed Moradi ◽  
Denis Laurendeau ◽  
Clement Gosselin
Keyword(s):  
Estimation Method ◽  
Closed Form Solution ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
Form Solution ◽  
Normal Vector ◽  
Open Problems ◽  
Robust Solution ◽  
Geometric Primitives ◽  
Cylinder Detection

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.

scholarly journals A Closed-Form Solution to Planar Feature-Based Registration of LiDAR Point Clouds

2021 ◽  
Vol 10 (7) ◽  
pp. 435
Author(s):  
Yongbo Wang ◽  
Nanshan Zheng ◽  
Zhengfu Bian
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Simulated Data ◽  
Real Data ◽  
Point Clouds ◽  
Form Solution ◽  
Spatial Transformation ◽  
Dual Quaternions ◽  
Feature Based ◽  
Planar Feature

Since pairwise registration is a necessary step for the seamless fusion of point clouds from neighboring stations, a closed-form solution to planar feature-based registration of LiDAR (Light Detection and Ranging) point clouds is proposed in this paper. Based on the Plücker coordinate-based representation of linear features in three-dimensional space, a quad tuple-based representation of planar features is introduced, which makes it possible to directly determine the difference between any two planar features. Dual quaternions are employed to represent spatial transformation and operations between dual quaternions and the quad tuple-based representation of planar features are given, with which an error norm is constructed. Based on L2-norm-minimization, detailed derivations of the proposed solution are explained step by step. Two experiments were designed in which simulated data and real data were both used to verify the correctness and the feasibility of the proposed solution. With the simulated data, the calculated registration results were consistent with the pre-established parameters, which verifies the correctness of the presented solution. With the real data, the calculated registration results were consistent with the results calculated by iterative methods. Conclusions can be drawn from the two experiments: (1) The proposed solution does not require any initial estimates of the unknown parameters in advance, which assures the stability and robustness of the solution; (2) Using dual quaternions to represent spatial transformation greatly reduces the additional constraints in the estimation process.

scholarly journals Reconstruction of three-dimensional maps based on closed-form solutions of the variational problem of multisensor data registration

2019 ◽  
Vol 484 (6) ◽  
pp. 672-677
Author(s):  
A. V. Vokhmintcev ◽  
A. V. Melnikov ◽  
K. V. Mironov ◽  
V. V. Burlutskiy
Keyword(s):  
Closed Form ◽  
Large Scale ◽  
Three Dimensional ◽  
Dynamic Environment ◽  
Closed Form Solution ◽  
Point Clouds ◽  
Accurate Method ◽  
Form Solution ◽  
Closed Form Solutions ◽  
Reconstruction Methods

A closed-form solution is proposed for the problem of minimizing a functional consisting of two terms measuring mean-square distances for visually associated characteristic points on an image and meansquare distances for point clouds in terms of a point-to-plane metric. An accurate method for reconstructing three-dimensional dynamic environment is presented, and the properties of closed-form solutions are described. The proposed approach improves the accuracy and convergence of reconstruction methods for complex and large-scale scenes.

Collision-Free Multi-Robot Navigation Using Autonomous Object Selection

2013 ◽  
Vol 313-314 ◽  
pp. 918-922
Author(s):  
Kong Woo Lee ◽  
Jae D. Jeon ◽  
Doo Jin Kim ◽  
Beom Hee Lee
Keyword(s):  
Closed Form Solution ◽  
Point Clouds ◽  
Form Solution ◽  
Threshold Function ◽  
Selection Algorithm ◽  
Sift Algorithm ◽  
Object Selection ◽  
Depth Maps ◽  
Surface Segmentation ◽  
Multi Robot

For successful SLAM, landmarks for pose estimation should be continuously observed. Firstly, we proposed the object selection algorithm from 2D images and 3D depth maps without human’s supervision. We used the SIFT algorithm to obtain descriptors of point features inside the object, and the surface segmentation algorithm to obtain separated objects from point clouds of 3D depth maps. Automatically selected objects were tested by the threshold function using repeatability, distinctiveness and saliency whether the objects were suitable or not for reusing and sharing between the robots. Secondly, we suggested the closed-form solution to estimate the 3D pose of robots from the information of selected objects. Furthermore, we provided the effective way to accomplish the tasks using multi-robot by compensating the accumulated navigating errors and re-planning the collision-free motion of the robots using the extended collision map algorithm.

scholarly journals A WEIGHTED CLOSED-FORM SOLUTION FOR RGB-D DATA REGISTRATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 403-409 ◽  
Author(s):  
K. M. Vestena ◽  
D. R. Dos Santos ◽  
E. M. Oilveira Jr. ◽  
N. L. Pavan ◽  
K. Khoshelham
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Point Clouds ◽  
Form Solution ◽  
Body Motion ◽  
Random Errors ◽  
Scale Invariant ◽  
Loop Closure ◽  
Dual Number ◽  
Data Registration

Existing 3D indoor mapping of RGB-D data are prominently point-based and feature-based methods. In most cases iterative closest point (ICP) and its variants are generally used for pairwise registration process. Considering that the ICP algorithm requires an relatively accurate initial transformation and high overlap a weighted closed-form solution for RGB-D data registration is proposed. In this solution, we weighted and normalized the 3D points based on the theoretical random errors and the dual-number quaternions are used to represent the 3D rigid body motion. Basically, dual-number quaternions provide a closed-form solution by minimizing a cost function. The most important advantage of the closed-form solution is that it provides the optimal transformation in one-step, it does not need to calculate good initial estimates and expressively decreases the demand for computer resources in contrast to the iterative method. Basically, first our method exploits RGB information. We employed a scale invariant feature transformation (SIFT) for extracting, detecting, and matching features. It is able to detect and describe local features that are invariant to scaling and rotation. To detect and filter outliers, we used random sample consensus (RANSAC) algorithm, jointly with an statistical dispersion called interquartile range (IQR). After, a new RGB-D loop-closure solution is implemented based on the volumetric information between pair of point clouds and the dispersion of the random errors. The loop-closure consists to recognize when the sensor revisits some region. Finally, a globally consistent map is created to minimize the registration errors via a graph-based optimization. The effectiveness of the proposed method is demonstrated with a Kinect dataset. The experimental results show that the proposed method can properly map the indoor environment with an absolute accuracy around 1.5% of the travel of a trajectory.

scholarly journals A FREQUENCY-DRIFT COMPENSATED CLOSED-FORM SOLUTION FOR STEREO RGB-D MAPPING

2019 ◽  
Vol IV-2/W5 ◽  
pp. 293-297
Author(s):  
S. J. Tang ◽  
Q. Zhu ◽  
W. Chen ◽  
W. X. Wang ◽  
Y. Li ◽  
...  
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Point Clouds ◽  
Form Solution ◽  
Frequency Drift ◽  
Pose Tracking ◽  
Camera Pose ◽  
Indoor Scenarios ◽  
Spatially Variant ◽  
Orientation Parameters

<p><strong>Abstract.</strong> In this work, we present a frequency-drift compensated (Fd-C) closed-form solution for stereo RGB-D SLAM. The intrinsic parameters for each sensor are first obtained with a standard camera calibration process and the extrinsic orientation parameters achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. With the assumption of fix transformation between the frames with the same timestamp, we define one sensor as reference sensor and the other sensor as slave sensor and the slave frames can be mapped to the timeline of the references sensor. Rather than endow the camera pose of the nearest frame to the slave frames, we derive the accurate camera pose for slave frames in a spatially variant way. Therefore, the pose relations between the slave frame and the adjacent reference frame can be derived, which provided opportunity to use the more accuracy observations from multiple frames for better tracking and global optimization. We present the mathematical analysis of the iterative optimizations for pose tracking in multi-RGB-D camera cases. Finally, the experiments in complex indoor scenarios demonstrate the efficiency of the proposed multiple RGB-D slam algorithm.</p>

scholarly journals Robust affine registration method using line/surface normals and correntropy criterion

2022 ◽  
Author(s):  
Abdurrahman Yilmaz ◽  
Hakan Temeltas
Keyword(s):  
Closed Form Solution ◽  
Point Clouds ◽  
Form Solution ◽  
Least Square ◽  
3D Registration ◽  
Point Sets ◽  
Registration Method ◽  
Affine Registration ◽  
Surface Normal ◽  
Application Procedure

AbstractThe problem of matching point clouds is an efficient way of registration, which is significant for many research fields including computer vision, machine learning, and robotics. There may be linear or non-linear transformation between point clouds, but determining the affine relation is more challenging among linear cases. Various methods have been presented to overcome this problem in the literature and one of them is the affine variant of the iterative closest point (ICP) algorithm. However, traditional affine ICP variants are highly sensitive to effects such as noises, deformations, and outliers; the least-square metric is substituted with the correntropy criterion to increase the robustness of ICPs to such effects. Correntropy-based robust affine ICPs available in the literature use point-to-point metric to estimate transformation between point clouds. Conversely, in this study, a line/surface normal that examines point-to-curve or point-to-plane distances is employed together with the correntropy criterion for affine point cloud registration problems. First, the maximum correntropy criterion measure is built for line/surface normal conditions. Then, the closed-form solution that maximizes the similarity between point sets is achieved for 2D registration and extended for 3D registration. Finally, the application procedure of the developed robust affine ICP method is given and its registration performance is examined through extensive experiments on 2D and 3D point sets. The results achieved highlight that our method can align point clouds more robustly and precisely than the state-of-the-art methods in the literature, while the registration time of the process remains at reasonable levels.

Closed Form Solution of the Exterior-Point Eshelby Tensor for an Elliptic Cylindrical Inclusion

2009 ◽  
Vol 77 (2) ◽  
Author(s):  
B. R. Kim ◽  
H. K. Lee
Keyword(s):  
Closed Form ◽  
Closed Form Solution ◽  
Form Solution ◽  
Eshelby Tensor ◽  
Cylindrical Inclusion ◽  
Normal Vector ◽  
Eshelby’S Tensor ◽  
Cylindrical Inclusions ◽  
Outward Unit ◽  
Outward Unit Normal Vector

With the help of the I-integrals expressed by Mura (1987, Micromechanics of Defects in Solids, 2nd ed., Martinus Nijhoff, Dordrecht) and the outward unit normal vector introduced by Ju and Sun (1999, “A Novel Formulation for the Exterior-Point Eshelby’s Tensor of an Ellipsoidal Inclusion,” ASME Trans. J. Appl. Mech., 66, pp. 570–574), the closed form solution of the exterior-point Eshelby tensor for an elliptic cylindrical inclusion is derived in this work. The proposed closed form of the Eshelby tensor for an elliptic cylindrical inclusion is more explicit than that given by Mura, which is rough and unfinished. The Eshelby tensor for an elliptic cylindrical inclusion can be reduced to the Eshelby tensor for a circular cylindrical inclusion by letting the aspect ratio of the inclusion α=1. The closed form Eshelby tensor presented in this study can contribute to micromechanics-based analysis of composites with elliptic cylindrical inclusions.

scholarly journals A Closed-Form Solution to Linear Feature-Based Registration of LiDAR Point Clouds

2021 ◽  
Vol 13 (18) ◽  
pp. 3571
Author(s):  
Yongbo Wang ◽  
Nanshan Zheng ◽  
Zhengfu Bian ◽  
Hua Zhang
Keyword(s):  
Objective Function ◽  
Closed Form ◽  
Closed Form Solution ◽  
Point Clouds ◽  
Form Solution ◽  
Linear Features ◽  
Registration Method ◽  
Linear Feature ◽  
Feature Based ◽  
Transformation Parameters

Due to the high complexity of geo-spatial entities and the limited field of view of LiDAR equipment, pairwise registration is a necessary step for integrating point clouds from neighbouring LiDAR stations. Considering that accurate extraction of point features is often difficult without the use of man-made reflectors, and the initial approximate values for the unknown transformation parameters must be estimated in advance to ensure the correct operation of those iterative methods, a closed-form solution to linear feature-based registration of point clouds is proposed in this study. Plücker coordinates are used to represent the linear features in three-dimensional space, whereas dual quaternions are employed to represent the spatial transformation. Based on the theory of least squares, an error norm (objective function) is first constructed by assuming that each pair of corresponding linear features is equivalent after registration. Then, by applying the extreme value analysis to the objective function, detailed derivations of the closed-form solution to the proposed linear feature-based registration method are given step by step. Finally, experimental tests are conducted on a real dataset. The derived experimental result demonstrates the feasibility of the proposed solution: By using eigenvalue decomposition to replace the linearization of the objective function, the proposed solution does not require any initial estimates of the unknown transformation parameters, which assures the stability of the registration method.

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