Improved Map Generation by Addition of Gaussian Noise for Indoor SLAM using ROS

DOI

10.2991/jrnal.2017.4.2.3How to use a DOI?

Keywords

SLAM, ROS, Gaussian Noise, map generation, exploration.

Abstract

Rao-Blackwellized Particle Filter (RBPF) is used in this paper to solve the Simultaneous Localization and Mapping (SLAM) problem. RBPF algorithm uses particle filter where each particle carries an individual map of the environment. With the usage of Robot Operating System (ROS), GMapping package was used as a basis for map generation and SLAM. To improve the map generation, Gaussian noise was introduced to the data from laser range finder and also the odometry from the robot Pioneer P3AT’s pose. The introduced algorithm was successful in decreasing the uncertainty as well as increased the knowledge of each particle in the estimation of the robot’s pose, proven through practical experiment. Exploration experiments were also carried out to test the performance of P3AT based on our proposed method.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Barry Loh Tze Yuen
AU  - Khairul Salleh Mohamed Sahari
AU  - Zubaidi Faiesal Mohamad Rafaai
PY  - 2017
DA  - 2017/09/01
TI  - Improved Map Generation by Addition of Gaussian Noise for Indoor SLAM using ROS
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 118
EP  - 123
VL  - 4
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.2.3
DO  - 10.2991/jrnal.2017.4.2.3
ID  - Yuen2017
ER  -