MATEC Web Conf.
Volume 161, 201813th International Scientific-Technical Conference on Electromechanics and Robotics “Zavalishin’s Readings” - 2018
|Number of page(s)||6|
|Published online||18 April 2018|
Adaptive particle filter for localization problem in service robotics
Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics - Intelligent Process Control and Robotics Lab (IARIPR), Engler-Bunte-Ring 8, 76131 Karlsruhe, Germany
2 SPIIRAS, Laboratory of Autonomous Robotic Systems, 199178, Saint-Petersburg, 14-th line of V.I., 39, Russia
* Corresponding author: firstname.lastname@example.org
In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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