Improved Map Generation by Addition of Gaussian Noise for Indoor SLAM using ROS
- 10.2991/jrnal.2017.4.2.3How to use a DOI?
- SLAM, ROS, Gaussian Noise, map generation, exploration.
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.
- © 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/).
Cite this article
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 -