RT Journal Article
T1 Differential evolution Markov chain filter for global localization
A1 Moreno Lorente, Luis Enrique
A1 Martín Monar, Fernando
A1 Muñoz, María Luisa
A1 Garrido Bullón, Luis Santiago
AB A key challenge for an autonomous mobile robot is to estimate its location according to the available information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evolution method that solves this problem in 2D and 3D environments. The robot’s pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Differential Evolution method to solve efficiently many optimization problems. In this work, we have combined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.
PB Springer
SN 0921-0296
YR 2016
FD 2016-06-01
LK https://hdl.handle.net/10016/34429
UL https://hdl.handle.net/10016/34429
LA eng
NO The research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.
DS e-Archivo
RD 27 jul. 2024