A three-dimensional point cloud registration based on entropy and particle swarm optimization

Zhan, Xu; Cai, Yong; He, Ping

doi:10.1177/1687814018814330

Cited by 24 publications

(10 citation statements)

References 36 publications

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“…To rule out these latter cases, we quantified the entropy in each cluster to exclude ones that present a high degree of disorder in his distribution. For entropy measurement, we used the formulation described in [43]. We show the clustering computation described, in line 16…”

Section: B Co-registration Of Camera-lidar Pointsmentioning

confidence: 99%

Targetless Camera-LiDAR Calibration in Unstructured Environments

2020

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The camera-Lidar sensor fusion plays an important role in autonomous navigation research. Nowadays, the automatic calibration of these sensors remains a significant challenge in mobile robotics. In this article, we present a novel calibration method that achieves an accurate six-degree-offreedom (6-DOF) rigid-body transformation estimation (aka extrinsic parameters) between the camera and LiDAR sensors. This method consists of a novel co-registration approach that uses local edge features in arbitrary environments to get 3D-to-2D errors between the data of both, camera and LiDAR. Once we have 3D-to-2D errors, we estimate the relative transform, i.e., the extrinsic parameters, that minimizes these errors. In order to find the best transform solution, we use the perspective-three-point (P3P) algorithm. To refine the final calibration, we use a Kalman Filter, which gives the system high stability against noise disturbances. The presented method does not require, in any case, an artificial target, or a structured environment, and therefore, it is a target-less calibration. Furthermore, the method we present in this article does not require to achieve a dense point cloud, which holds the advantage of not needing a scan accumulation. To test our approach, we use the state-of-the-art Kitti dataset, taking the calibration provided by the dataset as the ground truth. In this way, we achieve accuracy results, and we demonstrate the robustness of the system against very noisy observations.

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Section: B Co-registration Of Camera-lidar Pointsmentioning

confidence: 99%

Targetless Camera-LiDAR Calibration in Unstructured Environments

2020

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“…Traditional point cloud registration methods are often called optimization-based point cloud registration frameworks. Such registration algorithms [1][2][3][4][5][6][7] obtain the optimal transformation matrix by iterating through two phases [8] correspondence search and transformation estimation. The correspondence search finds the corresponding (matching) points between the point clouds.…”

Section: Introductionmentioning

confidence: 99%

PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration

Yang

Shi

et al. 2022

Applied Sciences

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Existing end-to-end cloud registration methods are often inefficient and susceptible to noise. We propose an end-to-end point cloud registration network model, Point Transformer for Registration Network (PTRNet), that considers local and global features to improve this behavior. Our model uses point clouds as inputs and applies a Transformer method to extract their global features. Using a K-Nearest Neighbor (K-NN) topology, our method then encodes the local features of a point cloud and integrates them with the global features to obtain the point cloud’s strong global features. Comparative experiments using the ModelNet40 data set show that our method offers better results than other methods, with a mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) between the ground truth and predicted values lower than those of competing methods. In the case of multi-object class without noise, the rotation average absolute error of PTRNet is reduced to 1.601 degrees and the translation average absolute error is reduced to 0.005 units. Compared to other recent end-to-end registration methods and traditional point cloud registration methods, the PTRNet method has less error, higher registration accuracy, and better robustness.

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“…There exist some 3-D medical image registration methods [13,14] that utilize several features in the registration process including pointcloud coordinates representing the 3-D shapes of objects. These coordinates have also been used in the registration process shown in [15][16][17][18][19][20]. All the mentioned research works utilize a variation of the particle swarm optimization (PSO) to find the matching location between the source and target images.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

et al. 2021

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The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer2 and 7.41 micrometer2 for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method.

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A three-dimensional point cloud registration based on entropy and particle swarm optimization

Cited by 24 publications

References 36 publications

Targetless Camera-LiDAR Calibration in Unstructured Environments

Targetless Camera-LiDAR Calibration in Unstructured Environments

PTRNet: Global Feature and Local Feature Encoding for Point Cloud Registration

Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

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