帝王蝶算法优化粒子滤波在SLAM中的应用研究

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

摘要/Abstract

摘要：

为解决传统粒子滤波重采样时易出现权值退化及粒子多样性丧失导致滤波精度下降，使机器人定位不准确及地图构建不精确的问题，提出一种基于改进帝王蝶算法优化的粒子滤波算法。以帝王蝶个体代替粒子个体，将帝王蝶算法中的迁移算子和调整算子融入粒子滤波算法中。在帝王蝶的迭代更新过程中引入自适应遗传参数，在粒子滤波重采样时采用线性组合优化重采样方法提高粒子多样性。结果表明：基于改进帝王蝶算法的粒子滤波算法与原算法相比预测精度和运行速度分别提高了29.7%及5.6%以上，应用于机器人定位与地图构建方面也能提高了40%以上的地图构建精度及10.5%的运行速度。

关键词: 粒子滤波, 帝王蝶算法, 重采样, 即时定位与地图构建

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

中图分类号:

TP242

陈志强, 曹梦龙, 赵文彬. 帝王蝶算法优化粒子滤波在SLAM中的应用研究[J]. 系统仿真学报, 2023, 35(6): 1351-1361.

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