Registration of GPS and Stereo Vision for Point Cloud Localization in Intelligent Vehicles Using Particle Swarm Optimization

John, Vijay; Xu, Yuquan; Mita, Seiichi; Qian, Long; Liu, Zheng

doi:10.1007/978-3-319-61824-1_23

Cited by 9 publications

(2 citation statements)

References 10 publications

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“…Since point cloud coordinates are used in our 3D teeth reconstruction, the 3D registration is more proper. In the literature, there are several works on 3D registration [ 27 , 28 , 29 , 30 , 31 ] utilizing several features in the registration process, including point cloud coordinates representing the 3D shapes of objects [ 32 , 33 , 34 , 35 , 36 , 37 ]. These mentioned works used a variation swarm optimization (PSO) in the location matching between the source and target images.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Wongkhuenkaew,

Auephanwiriyakul,

Chaiworawitkul

et al. 2024

Bioengineering

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Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registration method is introduced. To refine the 3D registration result, we incorporate the iterative closet point (ICP). The BCDL-GWO with ICP method is implemented on the scanned commercial orthodontic tooth and regular tooth models. Since this is a registration from multi-views of optical images, the hierarchical structure is implemented. According to the results for both models, the proposed algorithm produces high-quality 3D visualization images with the smallest mean squared error of about 7.2186 and 7.3999 μm2, respectively. Our results are compared with the statistical randomization-based particle swarm optimization (SR-PSO). The results show that the BCDL-GWO with ICP is better than those from the SR-PSO. However, the computational complexities of both methods are similar.

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Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Wongkhuenkaew,

Auephanwiriyakul,

Chaiworawitkul

et al. 2024

Bioengineering

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show abstract

“…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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Stereo vision-based vehicle localization in point cloud maps using multiswarm particle swarm optimization

John

Liu

Mita

et al. 2019

SIViP

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Registration of GPS and Stereo Vision for Point Cloud Localization in Intelligent Vehicles Using Particle Swarm Optimization

Cited by 9 publications

References 10 publications

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

Grey Wolf Optimizer with Behavior Considerations and Dimensional Learning in Three-Dimensional Tooth Model Reconstruction

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

Stereo vision-based vehicle localization in point cloud maps using multiswarm particle swarm optimization

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