Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7117003 | The Journal of China Universities of Posts and Telecommunications | 2016 | 9 Pages |
Abstract
For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized Hâ particle filter (IRBHF-SLAM) algorithm is proposed. The iterated unscented Hâ filter (IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor's observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM (UFastSLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system (ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.
Related Topics
Physical Sciences and Engineering
Engineering
Electrical and Electronic Engineering
Authors
Luo Yuan, Su Qin, Zhang Yi, Zheng Xiaofeng,