Laser point cloud registration method based on iterative closest point improved by Gaussian mixture model considering corner features

Wang, Yongzhi; Zhou, Tao; Li, Hui; Tu, Wenlong; Xi, Jing; Liao, Lixia

doi:10.1080/01431161.2021.2022242

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…In [26], to improve the robustness of ICP, an inequality constraint of a rotation angle is introduced into the registration model, which is solved by an extended ICP algorithm. A 3D LiDAR point cloud data registration method based on the ICP algorithm, which is improved by the Gaussian mixture model (GMM) considering corner features, is proposed in [27] to address timeliness and local optima. In [28], a hybrid approach which combines ICP-based registration with feature extraction and surface reconstruction is proposed to deal explicitly with the inhomogeneous density of point clouds produced by LiDAR scanners.…”

Section: Points Cloud-based Registrationmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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Considering that laser scan stations are expensive and heavy for the indoor environment reconstruction of unmanned systems, a low-cost and light reconstruction system is proposed in this research. The system consists of a cross-structured light visual (SLV) sensor and an inertial navigation system (INS). The cross-SLV sensor is used to scan the surroundings and to estimate the angle change between two adjacent measurements. To improve the robustness and accuracy of the angle measurement, a Kalman Filter (KF) with simple construction is designed to fuse the inertial information from the INS. The factors which influence ranging accuracy are analyzed and ranging experiments show that the SLV sensor has an accuracy of higher than 2% when the distance is less than 4 m. Then the reconstruction results of a kitchen and corridor show that the error of most points is less than 50 mm for either kitchen (94%) or corridor (85%), and the mean errors and standard deviations of kitchen and corridor are less than 20 mm and 30 mm, respectively.

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Section: Points Cloud-based Registrationmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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“…To address the problem that the ICP algorithm falls into the local minimum, Ref. 43 improves it by a Gaussian mixture model that considers corner features. Reference 44 and 45 use deep learning methods to improve the nearest point search.…”

Section: Related Workmentioning

confidence: 99%

Two-stage outlier removal strategy for correspondence-based point cloud registration

Li,

Chen,

Gao

2023

J. Appl. Rem. Sens.

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.With the emergence of keypoint matching technology, the correspondence-based point cloud registration (PCR) method has gained increasing attention. However, the correspondences generated by keypoint matching technology contain extremely high outlier rates, resulting in the correspondence-based PCR method facing issues of high computational complexity and low precision registration. We propose a correspondence-based PCR method using a coarse-to-fine outlier removal strategy with O ( N ) complexity. First, we propose a coarse outlier removal module based on linearly related properties, i.e., we build a deviation matrix that can measure each correspondence deviation the degree away from the ideal inlier. The module can reduce the number of correspondences and the outlier rates. Then, we propose a fine outlier removal module that adopts each correspondence to identify outlier based on the spatial geometric mapping invariance. Finally, to increase registration accuracy, we introduce a graduated non-convexity with Tukey’s biweight method. It can avoid the solution falling into the local minimum and better reduce the influence of outliers. Experimental results show that the proposed method is robust at outlier rates above 99% and is faster than state-of-the-art methods.

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“…However, the performance of two-dimensional methods in a three-dimensional space is somewhat limited, particularly in terms of keypoint repeatability. To address the issue of a limited number of keypoints detected by Harris 3D, Wang et al [12] proposed the nearest neighbor search (NNS) Harris 3D algorithm, which enhances the original Harris 3D keypoints by including multiple neighboring points as keypoints. Chen et al [13] introduced the local surface patches (LSP) keypoint detection algorithm, which employs point shape indices to identify candidate keypoints and utilizes non-maximum suppression (NMS) to select the final keypoints.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Registration Based on Local Variation of Surface Keypoints

Zhu,

Huang,

Yue

et al. 2023

Electronics

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Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency.

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Laser point cloud registration method based on iterative closest point improved by Gaussian mixture model considering corner features

Cited by 12 publications

References 30 publications

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Two-stage outlier removal strategy for correspondence-based point cloud registration

Point Cloud Registration Based on Local Variation of Surface Keypoints

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