An Improved Poisson Surface Reconstruction Algorithm based on the Boundary Constraints

Liu, Zhouqi; Wang, Lei; Tahir, Muhammad Nawaz; Huang, Jin; Cheng, Tianqi; Xia-zhen, Guo; Wang, Yuwei; Liu, ChunXiang

doi:10.14569/ijacsa.2023.0140126

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…The ancient route technique known as the "ant colonies program" was created by summarizing how ants forage in the wild [21][22][23][24][25][26][27]. The selection basis is that the shorter route has higher pheromone concentration along the route, which guides other ants to forage along the route and forms positive feedback to obtain the shortest route to find food.…”

Section: Improved Algorithm Compared With Other Algorithmsmentioning

confidence: 99%

Towards Path Planning Algorithm Combining with A-Star Algorithm and Dynamic Window Approach Algorithm

Li¹,

Gong²,

Tahir³

et al. 2023

IJACSA

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In the Automated Guided Vehicle (AGV) warehouse automatic guided vehicle system, the path planning algorithm for intelligent logistics vehicles is a key factor to ensure the stable and efficient operation of the system. However, the existing planning algorithms have problems such as single designing a route and the inability to intelligently evade moving barriers. The academic community has proposed various solutions to these problems, although they have improved the efficiency and quality of path planning to some extent, they have not completely solved problems such as poor safety in planning, the high number of path inflection points, poor path smoothness, easily getting stuck in deadlocks, and have not fully considered the running cost and practical implementation difficulty of algorithms. To address these issues, the article deeply researched traditional A* scheme and Dynamic Window Approach (DWA) technology and proposed designing a route method according to the fusion of the A* algorithm and DWA technology. The algorithm improved the A algorithm by introducing a sub-node optimization algorithm to solve problems for instance poor global path planning safety and easy deadlock. Moreover, the algorithm reduced the amount of global route reversal locations and increased path consistency by improving the evaluation function and removing redundant points of the A algorithm. Finally, by integrating the DWA algorithm, the intelligent logistics vehicle achieved dynamic obstacle avoidance capabilities for moving objects in the real world. Our simulations-based results on MATLAB framework show that the algorithm significantly improves path smoothness, path length, path planning time, and environmental adaptability compared to traditional algorithms, and basically meets the path planning requirements of the AGV system for intelligent logistics vehicles.

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Section: Improved Algorithm Compared With Other Algorithmsmentioning

confidence: 99%

Towards Path Planning Algorithm Combining with A-Star Algorithm and Dynamic Window Approach Algorithm

Li¹,

Gong²,

Tahir³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…As one of the most faithful and convenient data formats, the datasets of point clouds have been popularly applied in the domain of 3D reconstruction [1,2], virtual reality [3], augmented reality [4], etc. Due to environmental and other influence factors, the registration of point cloud is an essential step before various tasks of computer vision and robotics applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel and Efficient Point Cloud Registration by using Coarse-to-Fine Strategy Integrating PointNet

Liu,

Cheng,

Tahir

et al. 2023

IJACSA

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The registration of the point cloud plays a critical and fundamental role in the computer vision domain. Although quite good registration results have been obtained by using the global, local, and learning-based registration strategies, there are still many problems to solve. For example, the local methods that are based on geometric features are very sensitive to attitude deviation, the global shapes-based methods are easy to result in inconsistency when the distribution differences are obvious and the learning-based registration methods have highly relied on the huge label data. A novel and effective registration method for the point cloud data integrating the coarse-to-fine strategy and the improved PointNet network is proposed to overcome the abovementioned drawbacks and improve registration accuracy. The improved Random Sample Consensus (RANSAC) algorithm is developed to effectively deal with the initial attitude deviation problem in the coarse registration procedure and the improved Lucas and Kanade (LK) algorithm is proposed based on the classical PointNet framework to reduce the errors of the refine registration, and the whole registration procedure is implemented under a trainable recurrent deep learning architecture. Compared with the state-of-the-art point cloud registration methods, experimental results fully prove that the proposed method can effectively handle the significant attitude deviation and partial overlap problem and achieves stronger robustness and higher accuracy.

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An Improved Poisson Surface Reconstruction Algorithm based on the Boundary Constraints

Cited by 2 publications

References 35 publications

Towards Path Planning Algorithm Combining with A-Star Algorithm and Dynamic Window Approach Algorithm

Towards Path Planning Algorithm Combining with A-Star Algorithm and Dynamic Window Approach Algorithm

A Novel and Efficient Point Cloud Registration by using Coarse-to-Fine Strategy Integrating PointNet

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