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Robot Pose Estimation Based on Visual Information and Particle Swarm Optimization

Carlos Lopez-Franco1, Javier Gomez-Avila2, Nancy Arana-Daniel3, Alma Y. Alanis

Computer Science Department, CUCEI, University of Guadalajara. Blvd. Marcelino Garcia Barragan 1421, Col. Olimpica, Guadalajara, Jalisco, Mexico, C. P. 44430
Email: 1 carlos.lopez@cucei.udg.mx, 2 jegomezavila@gmail.com, 3 nancy.arana@cucei.udg.mx

* Corresponding Author: Alma Y. Alanis, email

Intelligent Automation & Soft Computing 2018, 24(2), 431-442. https://doi.org/10.31209/2018.100000000

Abstract

This paper presents a method for 3D pose estimation using visual information and a soft-computing algorithm. The algorithm uses quaternions to represent rotations, and Particle Swarm Optimization to estimate such quaternion. The rotation estimation problem is cast as a minimization problem, which finds the best quaternion for the given data using the PSO algorithm. With this technique, the algorithm always returns a valid quaternion, and therefore a valid rotation. During the estimation process, the algorithm is able to detect and reject outliers. The simulations and experimental results show the robustness of algorithm against noise and outliers.

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Cite This Article

C. Lopez-Franco, J. Gomez-Avila, N. Arana-Daniel and A. Y. Alanis, "Robot pose estimation based on visual information and particle swarm optimization," Intelligent Automation & Soft Computing, vol. 24, no.2, pp. 431–442, 2018.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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