Open Access
ARTICLE
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,
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.
Keywords
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.