loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Anas W. Alhashimi ; Roland Hostettler and Thomas Gustafsson

Affiliation: Luleå University of Technology, Sweden

ISBN: 978-989-758-040-6

Keyword(s): Localization, Robotics, Particle Filter, Monte Carlo Localization, Sensor Model, Observation Model.

Related Ontology Subjects/Areas/Topics: Autonomous Agents ; Informatics in Control, Automation and Robotics ; Mobile Robots and Autonomous Systems ; Robotics and Automation

Abstract: Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that s electing suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.206.194.210

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
W. Alhashimi, A.; Hostettler, R. and Gustafsson, T. (2014). An Improvement in the Observation Model for Monte Carlo Localization.In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 498-505. DOI: 10.5220/0005065604980505

@conference{icinco14,
author={Anas W. Alhashimi. and Roland Hostettler. and Thomas Gustafsson.},
title={An Improvement in the Observation Model for Monte Carlo Localization},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={498-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005065604980505},
isbn={978-989-758-040-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - An Improvement in the Observation Model for Monte Carlo Localization
SN - 978-989-758-040-6
AU - W. Alhashimi, A.
AU - Hostettler, R.
AU - Gustafsson, T.
PY - 2014
SP - 498
EP - 505
DO - 10.5220/0005065604980505

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.