@inproceedings{0be35bdd85514df88829ddb92383d838,
title = "Mobile robot global localization using particle filters",
abstract = "Mobile robot global localization is the problem of determining a robot's pose in an environment by using sensor data, when the initial position is unknown. Particle filter based Probabilistic algorithm called Monte Carlo Localization is the current popular approach to solve the robot localization problem. In this paper we introduce the multi-sensor based Monte Carlo Localization (MCL) method which represents a robot's belief by a set of weighted samples and use the Laser Range Finder (LRF) sensor to measurement update. We also proposed likelihood based particle filter to solve the kidnapped problem. The experiment results illustrate the efficiency and robustness of particle filter approach for our mobile robot.",
keywords = "Global localization, Likelihood, Mobile robot, Particle filter",
author = "Guanghui Cen and Nobuto Matsuhira and Junko Hirokawa and Hideki Ogawa and Ichiro Hagiwara",
year = "2008",
doi = "10.1109/ICCAS.2008.4694593",
language = "English",
isbn = "9788995003893",
series = "2008 International Conference on Control, Automation and Systems, ICCAS 2008",
pages = "710--713",
booktitle = "2008 International Conference on Control, Automation and Systems, ICCAS 2008",
note = "2008 International Conference on Control, Automation and Systems, ICCAS 2008 ; Conference date: 14-10-2008 Through 17-10-2008",
}