By Topic

Simultaneous localization and mapping for mobile robot based on an improved particle filter algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zhong Min Wang ; College of Mechanical Engineering, Tianjin University of Technology and Education, 300222, China ; De Hua Miao ; Zhi Jiang Du

Simultaneous localization and mapping (SLAM) is an important topic in the autonomous mobile robot research. An improved Rao-Blackwellised particle filter (IRBPF) algorithm is proposed for the mobile robot to SLAM, which can simultaneously localize the robot and build up the map in the structured indoor environment. Firstly, IRBPF respectively uses particle filters (PF) to estimate the posterior probability distributions of robot postures and landmarks in the environment map. Secondly, an adaptive re-sampling technique is used to reduce the times of re-sampling so as to maintain a reasonable speed of samples, thus it reduce the risk of sample depletion. Finally, a robust motion model and an observation model with only ranging sensor and odometer are constructed. Experiment results indicate that the IRBPF algorithm builds the consistent map and modified the precision and real-time performance of localization and mapping, the SLAM results show the efficiency of this IRBPF algorithm.

Published in:

2009 International Conference on Mechatronics and Automation

Date of Conference:

9-12 Aug. 2009