Authors
Barry Loh Tze Yuen, Khairul Salleh Mohamed Sahari, Zubaidi Faiesal Mohamad
Rafaai
Corresponding Author
Barry Loh Tze Yuen
Available Online 1 September 2017.
DOI
https://doi.org/10.2991/jrnal.2017.4.2.3
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 ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).