A comparison of global optimizers applied to point cloud registration

Pablo Barrios, Vicente Guzman, Martin Adams, Martin Rudorfer

Research output: Chapter in Book/Published conference outputConference publication

Abstract

The registration of point cloud data is essential in various applications, such as computer vision and robotics. The Iterative Closest Point (ICP) algorithm offered a solution to this problem, with several subsequent methods addressing problems including occlusions and variable point data overlap. To also account for detection errors, the Particle Swarm Optimization - Cardinalized Optimal Linear Assignment (PSO-COLA) point data registration algorithm was introduced. This algorithm offers robust registration solutions in the presence of data miss-detections and false alarms, but being based on a Particle Swarm Optimization (PSO) concept is susceptible to local minima problems. To address this problem, we propose the use of two additional meta-heuristic algorithms, namely Artificial Rabbit Optimisation (ARO) and Artificial Bee Colony (ABC), in
combination with the Cardinalized Optimal Linear Assignment (COLA) metric. Our experiments show that the resulting ARO-COLA algorithm reduces the execution time compared with the former PSO-COLA algorithm while maintaining high registration accuracy, especially in scenarios with cardinality and spatial errors. The results indicate that the ARO-COLA algorithm is
a promising alternative for efficient and accurate point cloud registration.
Original languageEnglish
Title of host publicationConference Proceedings of the 13th International Conference on Control, Automation and Information Sciences (ICCAIS)
PublisherIEEE
Number of pages5
Publication statusPublished - 26 Nov 2024

Publication series

NameProceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS)
ISSN (Print)2475-7896
ISSN (Electronic)2475-790X

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