Bingham Distribution-Based Linear Filter for Online Pose Estimation

Rangaprasad, Arun Srivatsan; Xu, Mengyun; Zevallos-Roberts, Nicolas; Choset, Howie

doi:10.15607/rss.2017.xiii.016

Cited by 18 publications

(4 citation statements)

References 21 publications

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“…Sparse ICP [BTP13] can effectively solve the registration problem of sparse point clouds. Simultaneously, several methods [JH02, RXZC17, ZL18] have been offered to improve the calculation efficiency. However, these methods do not address the sensitivity to initial disturbances.…”

Section: Related Workmentioning

confidence: 99%

HDRNet: High‐Dimensional Regression Network for Point Cloud Registration

Jin

Zhang

Liu

et al. 2022

Computer Graphics Forum

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Abstract‐3D point cloud registration is a crucial topic in the reverse engineering, computer vision and robotics fields. The core of this problem is to estimate a transformation matrix for aligning the source point cloud with a target point cloud. Several learning‐based methods have achieved a high performance. However, they are challenged with both partial overlap point clouds and multiscale point clouds, since they use the singular value decomposition (SVD) to find the rotation matrix without fully considering the scale information. Furthermore, previous networks cannot effectively handle the point clouds having large initial rotation angles, which is a common practical case. To address these problems, this paper presents a learning‐based point cloud registration network, namely HDRNet, which consists of four stages: local feature extraction, correspondence matrix estimation, feature embedding and fusion and parametric regression. HDRNet is robust to noise and large rotation angles, and can effectively handle the partial overlap and multi‐scale point clouds registration. The proposed model is trained on the ModelNet40 dataset, and compared with ICP, SICP, FGR and recent learning‐based methods (PCRNet, IDAM, RGMNet and GMCNet) under several settings, including its performance on moving to invisible objects, with higher success rates. To verify the effectiveness and generality of our model, we also further tested our model on the Stanford 3D scanning repository.

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Section: Related Workmentioning

confidence: 99%

HDRNet: High‐Dimensional Regression Network for Point Cloud Registration

Jin

Zhang

Liu

et al. 2022

Computer Graphics Forum

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“…Classic registration algorithms for point clouds mainly include the iterative closest point (ICP) algorithm [11,12], variants of ICP [13][14][15][16][17][18][19] and geometry-based registration algorithms [20][21][22][23][24].…”

Section: Classic Registration Algorithmsmentioning

confidence: 99%

“…When the relative pose deviation of the pairwise point clouds is small, the algorithm is guaranteed convergence and can obtain an excellent registration result. Scholars have made various improvements to the ICP algorithm to enhance registration efficiency [16] and accuracy [17][18][19]. However, all ICP-style algorithms still rely on the direct calculation of the closest point correspondences; moreover, they cannot dynamically adjust according to the number of points and easily fall into local minima.…”

Section: Classic Registration Algorithmsmentioning

confidence: 99%

MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

Su¹,

Wang²,

Chen³

et al. 2021

Preprint

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With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

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“…However, the method cannot fit the deep learning frameworks well since the space of orientations lies on a nonlinear and closed manifold that belongs to a non‐Euclidean space [7]. For example, rotation matrices can lead to the discontinuity feature [8]; quaternions have a double embedding with the existence of two symmetric local minima [9]. Moreover, the accuracy of the orientation in some works may degrade when considering uncertainties of orientations, such as the rotational symmetries of objects.…”

Section: Introductionmentioning

confidence: 99%

Online deep Bingham network for probabilistic orientation estimation

Liu

Wei

et al. 2023

IET Computer Vision

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Orientation estimation is one of the core problems in several computer vision tasks. Recently deep learning techniques combined with the Bingham distribution have attracted considerable interest towards this problem when considering ambiguities and rotational symmetries of objects. However, existing works suffer from two issues. First, the computational overhead for calculating the normalisation constant of the Bingham distribution is relatively high. Second, the choice of loss functions is uncertain. In light of these problems, we present an online deep Bingham network to estimate the orientation of objects. We sharply reduce the computational overhead of the normalisation constant by directly applying a numerical integration formula. Additionally, we are the first to give theorems on the convexity and Lipschitz continuity of the Bingham distribution's negative log‐likelihood, which formally indicates that it is a proper choice of the loss function. We test our method on three public datasets, namely the UPNA, the T‐LESS and Pascal3D+, showing that our method outperforms the state‐of‐the‐art in terms of orientation accuracy and time efficiency, which can reduce the runtime by more than 6 h compared to the offline methods. The ablation experiments further demonstrate the effectiveness and robustness of our model.

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Bingham Distribution-Based Linear Filter for Online Pose Estimation

Cited by 18 publications

References 21 publications

HDRNet: High‐Dimensional Regression Network for Point Cloud Registration

HDRNet: High‐Dimensional Regression Network for Point Cloud Registration

MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

Online deep Bingham network for probabilistic orientation estimation

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