Performance of Genetic Algorithm and Simulated Annealing Based HybridPoint Cloud Registration Algorithms

Publish Year: 1401
نوع سند: مقاله کنفرانسی
زبان: English
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IAAMM02_058

تاریخ نمایه سازی: 17 مرداد 1401

Abstract:

Point cloud registration has an application in a wide variety of industrial sectors including manufacturing,building, architecture, robotics, and even medical imaging and analysis. The iterative closest point (ICP) isa well-known algorithm that is used for registration purposes. However, ICP is slow and the result is highlydependent on the initial condition. In recent years, several hybrid ICP algorithms have been proposed andinvestigated to overcome the disadvantages. Using metaheuristic optimization algorithms such as geneticalgorithm (GA) and simulated annealing (SA) to hybrid the registration procedure is among the bestsolutions to address the shortages of the conventional ICP algorithm. To increase the efficiency and precisionof the point cloud registration, it is needed to compare the performances of these new hybrid algorithms indepth. In the present study, the performance of two popular classes of hybrid ICP algorithms including GAICPand SA-ICP are presented based on the recent research that is available in the literature. The reason forchoosing these two optimization approaches is the popularity and relevant simplicity of these twooptimization algorithms. The efficiency and precision of proposed algorithms are compared and benefitsand shortages are discussed. Finally, the future works that cloud help to propose point cloud registrationalgorithms with higher efficiency and precision are suggested. The results suggest that the simulatedannealing is more efficient compared to other metaheuristic optimization methods if the setting parametersare tuned well.

Authors

Moosa Sajed

Mechanical Engineering Department, Azarbaijan Shahid Madani University;

M.A Saeimi-Sadigh

Mechanical Engineering Department, Azarbaijan Shahid Madani University;