Research on Application of Monarch Butterfly Optimization Particle Filter in SLAM

doi:10.16182/j.issn1004731x.joss.22-0090

Abstract

Abstract:

In traditional particle filter resampling, weight degradation and loss of particle diversity are prone to occur, which leads to the decrease in filtering accuracy and result in inaccurate robot positioning and inaccurate mapping. An optimized particle filter algorithm based on the improved monarch butterfly algorithm is proposed. The algorithm replaces the particle individual with the monarch butterfly individual, and integrates the migration operator and adjustment operator in the monarch butterfly algorithm into the particle filter algorithm. The adaptive genetic parameters are introduced to the iterative update process of the monarch butterfly, and the linear combination optimization resampling method is used to improve the particle diversity during particle filter resampling. It is verified by simulation experiments that compared with the original algorithm, the proposed particle filter algorithm based on the improved monarch butterfly algorithm improves the prediction accuracy and running speed by more than 29.7% and 5.6% respectively. It can also improve the mapping accuracy by more than 40% and the running speed by 10.5% when applied to the robot simultaneous localization and mapping.

Key words: particle filter, monarch butterfly optimization, resampling, simultaneous localization and mapping

CLC Number:

TP242

Zhiqiang Chen, Menglong Cao, Wenbin Zhao. Research on Application of Monarch Butterfly Optimization Particle Filter in SLAM[J]. Journal of System Simulation, 2023, 35(6): 1351-1361.

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算法	N=50	N =100	N =150
PF	3.271 6	2.674 9	2.380 5
BA-PF	2.594 7	1.606 6	1.543 9
MBO-PF	2.260 7	1.548 5	1.479 1
IMBO-PF	1.450 4	1.088 3	0.907 7

算法	N=50	N =100	N =150
PF	0.053 8	0.092 5	0.147 9
BA-PF	0.101 4	0.153 6	0.199 1
MBO-PF	0.095 6	0.158 5	0.201 6
IMBO-PF	0.086 9	0.145 0	0.190 3

算法	真实坐标	估计坐标	误差距离/m
PF-FastSLAM	(-5， 4)	(-5.67， 2.58)	1.57
BA-FastSLAM	(-5， 4)	(-3.98， 3.03)	1.41
MBO-FastSLAM	(-5， 4)	(-4.75， 4.53)	0.59
IMBO-FastSLAM	(-5， 4)	(-4.73， 4.22)	0.35

算法	真实坐标	估计坐标	误差距离/m
PF-FastSLAM	(4， -5)	(5.97， -3.41)	2.53
BA-FastSLAM	(4， -5)	(5.07， -6.35)	1.72
MBO-FastSLAM	(4， -5)	(4.35， -4.10)	0.97
IMBO-FastSLAM	(4， -5)	(4.50， -4.99)	0.50

算法	真实坐标	估计坐标	误差距离/m
PF-FastSLAM	(5， 6)	(3.28， 7.20)	2.09
BA-FastSLAM	(5， 6)	(3.83， 7.17)	1.65
MBO-FastSLAM	(5， 6)	(4.06， 6.36)	1.01
IMBO-FastSLAM	(5， 6)	(5.27， 6.23)	0.35