Parallel Optimization and Implementation of SLAM Algorithm Based on Particle Filter

Zhu Fu-li; Zeng Bi; Cao Jun

doi:10.12052/gdutxb.160055

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Parallel Optimization and Implementation of SLAM Algorithm Based on Particle Filter

更新时间：2026-05-27

- Parallel Optimization and Implementation of SLAM Algorithm Based on Particle Filter
- Vol. 34, Issue 2, Pages: 92-96(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.160055
  CLC： TP301
- Online First：09 March 2017，
  
  Published：2017
- 稿件说明：
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朱福利, 曾碧, 曹军. 基于粒子滤波的SLAM算法并行优化与实现[J]. 广东工业大学学报, 2017, 34(2): 92-96.

Zhu Fu-li, Zeng Bi, Cao Jun. Parallel Optimization and Implementation of SLAM Algorithm Based on Particle Filter[J]. 2017, 34(2): 92-96.
朱福利, 曾碧, 曹军. 基于粒子滤波的SLAM算法并行优化与实现[J]. 广东工业大学学报, 2017, 34(2): 92-96. DOI： 10.12052/gdutxb.160055.

Zhu Fu-li, Zeng Bi, Cao Jun. Parallel Optimization and Implementation of SLAM Algorithm Based on Particle Filter[J]. 2017, 34(2): 92-96. DOI： 10.12052/gdutxb.160055.

摘要

基于粒子滤波的即时定位与地图构建（simultaneous localization and mapping，SLAM）算法，可在完全未知的环境进行即时的定位和地图构建.该算法使用粒子集表示定位位姿的概率分布情况，计算量与粒子集的规模成正比，在一定范围内，粒子的数量越多，算法的定位准确度和抗干扰能力越好，但在增加粒子数量的同时，将增加计算时间，从而导致定位延迟，造成移动机器人的定位误差.提出一种结合粒子滤波和SLAM算法特点的GPU并行优化的方法进行加速，从而减少计算带来的定位延迟和定位误差.通过实验，证明使用GPU并行计算的算法改进有明显效果.

Abstract

Simultaneous localization and mapping is a new type of mobile robot localization method

which can obtain data through the mobile robot's own sensors and simultaneous localization and map building in a completely unknown environment. Based on PF-SLAM algorithm

the probability distribution of the location pose is expressed by the particle set

and the calculated amount is proportional to the size of the particle set

and also the number of particles determines the algorithm's location accuracy and anti-jamming capability. At the same time

increasing the number of particles will increase the computing time

which will lead to the positioning delay and the positioning error of the mobile robot. A method is presented to improve the algorithm by using GPU parallel computing

which can reduce the calculation time

thereby to reduce the positioning error caused by positioning delay. Experimental results show that the improved algorithm of GPU parallel computing has a significant effect.

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references

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