An Improved RBPF-SLAM Algorithm for Indoor Robot

Aiming at the problems of inaccurate proposal distribution and particle degradation of traditional RPF-SLAM algorithm in robot indoor mapping, an improved RPF-SLAM algorithm was proposed. Firstly, the motion model and observation model were combined as the mixed suggestion distribution. Annealing pa...

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Bibliographic Details
Published in2022 8th International Conference on Control Science and Systems Engineering (ICCSSE) pp. 90 - 94
Main Authors Guan, Chenxi, Wang, Shuying
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.07.2022
Subjects
Annealing
Control systems
Degradation
indoor robot
Markov processes
MCMC
Modeling
Monte Carlo methods
particle degradation
Proposals
proposed distribution
RBPF-SLAM
Online AccessGet full text
DOI10.1109/ICCSSE55346.2022.10079166

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Summary:Aiming at the problems of inaccurate proposal distribution and particle degradation of traditional RPF-SLAM algorithm in robot indoor mapping, an improved RPF-SLAM algorithm was proposed. Firstly, the motion model and observation model were combined as the mixed suggestion distribution. Annealing parameters were used to optimize the mixed suggestion distribution and adjust the proportion of the two in the suggestion distribution to make the mixed suggestion distribution more accurate. Secondly, a Markov chain Monte Carlo (MCMC) resampling method is used to generate reasonable samples from the target distribution by constructing Markov chains, so that particles can move to different positions in each iteration to avoid particle degradation. Experimental results show that compared with the traditional RBPF-SLAM algorithm, the improved algorithm has lower root mean square error and calculation time, higher accuracy and better effect of map construction.
DOI:10.1109/ICCSSE55346.2022.10079166