Point Cloud Registration Algorithm Based on the Grey Wolf Optimizer

Feng, Yaqing; Tang, Jialin; Su, Binghua; Su, Qinglang; Zhou, Zhuang

doi:10.1109/access.2020.3013706

Cited by 16 publications

(10 citation statements)

References 26 publications

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“…Reference [14] created short connections to aggregate features in different layers. Reference [15] derives a resolution-based feature combination module and a boundary-preserving optimization strategy. Reference [16] iteratively aggregated deep features to exploit the complementary saliency information between multilevel features and features in each individual layer.…”

Section: Related Workmentioning

confidence: 99%

Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing

Fu¹

2022

Journal of Sensors

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To identify the most visually salient regions in a set of paired RGB and depth maps, in this paper, we propose a multimodal feature fusion supervised RGB-D image saliency detection network, which learns RGB and depth data by two independent streams separately, uses a dual-stream side-supervision module to obtain saliency maps based on RGB and depth features for each layer of the network separately, and then uses a multimodal feature fusion module to fuse the latter 3 layers of RGB and depth high-dimensional information to generate high-level significant prediction results. Experiments on three publicly available datasets show that the proposed network outperforms the current mainstream RGB-D saliency detection models with strong robustness due to the use of a dual-stream side-surveillance module and a multimodal feature fusion module. We use the proposed RGB-D SOD model for background defocusing in realistic scenes and achieve excellent visual results.

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Section: Related Workmentioning

confidence: 99%

Application and Analysis of RGB-D Salient Object Detection in Photographic Camera Vision Processing

Fu¹

2022

Journal of Sensors

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“…Feng et al proposed a point cloud registration algorithm based on gray wolf optimizer (GWO), which uses a centralization method to solve the translation matrix. Subsequently, the inherent shape features are employed to simplify the points of the initial point cloud model, and the quadratic sum of the distances between the corresponding points in the simplified point cloud is utilized as the objective function [63]. The various parameters of the rotation matrix are obtained through the GWO algorithm, which effectively balances the global and local optimization ability to obtain the optimal value in a short time.…”

Section: Registration Methods Based On Mathematical Solutionsmentioning

confidence: 99%

High-Precision 3D Reconstruction for Small-to-Medium-Sized Objects Utilizing Line-Structured Light Scanning: A Review

2021

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Three-dimensional reconstruction technology has demonstrated broad application potential in the industrial, construction, medical, forestry, agricultural, and pastural sectors in the last few years. High-quality digital point cloud information exists to help researchers to understand objects and environments. However, current research mainly focuses on making adaptive adjustments to various scenarios and related issues in the application of this technology rather than looking for further improvements and enhancements based on technical principles. Meanwhile, a review of approaches, algorithms, and techniques for high-precision 3D reconstruction utilizing line-structured light scanning, which is analyzed from a deeper perspective of elementary details, is lacking. This paper takes the technological path as the logical sequence to provide a detailed summary of the latest development status of each key technology, which will serve potential users and new researchers in this field. The focus is placed on exploring studies reconstructing small-to-medium-sized objects, as opposed to performing large-scale reconstructions in the field.

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“…Both Scale-ICP and GO-ICP are not suitable for registering point clouds that are affine transformed, so they are not compared here. We compared the following three methods Affine-ICP [14], MDAR [15], PCR-GW [19], Whitening-ICP [20] and CPD [22]. The registration result is shown in Fig.…”

Section: A Affine Registration Testmentioning

confidence: 99%

“…These ICPbased algorithms show good registration results on some simple point registration. Feng et al proposed the PCR-GW algorithm [19], the algorithm is based on the gray wolf optimizer to register point clouds, and it improves the registration speed and registration accuracy. Shu et al proposed the Whitening-ICP algorithm [20], the registration error of it was small and it reduced the computational complexity of point cloud affine registration.…”

mentioning

confidence: 99%