Advanced search
Start date
Betweenand


Comparing Vision-based Monte-Carlo Localization Methods

Full text
Author(s):
Almeida, Aislan C. ; Neto, Sylvio R. J. ; Bianchi, Reinaldo A. C. ; DoNascimento, TP ; Colombini, EL ; DeBrito, AV ; Garcia, LTD ; Sa, STD ; Goncalves, LMG
Total Authors: 9
Document type: Journal article
Source: 15TH LATIN AMERICAN ROBOTICS SYMPOSIUM 6TH BRAZILIAN ROBOTICS SYMPOSIUM 9TH WORKSHOP ON ROBOTICS IN EDUCATION (LARS/SBR/WRE 2018); v. N/A, p. 6-pg., 2018-01-01.
Abstract

The task of localization is a recurrent subject in the domain of autonomous robotics. Even though it is a common subject, the differences among domains causes the need of different algorithms to solve the localization problem. This paper proposes a implementation of the Monte-Carlo Localization algorithm in order to solve the localization problem for the domain of the RoboCup Humanoid Soccer KidSize. It implements methods to change the quantity of particles in function of their dispersion, to solve the robot kidnapping problem by scattering the particles and to predict the observation that leads to improvement of the confidence in the position of the robot. The experiments show that the proposed methods improved the localization process in comparison with traditional methods regarding the execution time of the algorithm and the quality of the estimated position. (AU)

FAPESP's process: 16/21047-3 - ALIS: Autonomous Learning in Intelligent System
Grantee:Anna Helena Reali Costa
Support Opportunities: Regular Research Grants
FAPESP's process: 16/18792-9 - Describing, representing and solving spatial puzzles
Grantee:Paulo Eduardo Santos
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE