Research on robot particle filtering localization technology based on laser scanning ranging

GE Guang-shuang; LI Zi-long; YANG Kai; MA Rui-xin

doi:10.13873/J.1000-9787(2017)12-0036-04

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Research on robot particle filtering localization technology based on laser scanning ranging

更新时间：2025-09-11

- Research on robot particle filtering localization technology based on laser scanning ranging
- Vol. 36, Issue 12, Pages: 36-39(2017)
- 作者机构：
  
  交通运输部天津水运工程科学研究所
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)12-0036-04
  CLC： TP242
- Published：2017
- 稿件说明：
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[1]戈广双,李子龙,杨凯,马瑞鑫.基于激光扫描测距的机器人粒子滤波定位技术研究[J].传感器与微系统,2017,36(12):36-39.

GE Guang-shuang, LI Zi-long, YANG Kai, et al. Research on robot particle filtering localization technology based on laser scanning ranging[J]. 2017, 36(12): 36-39.
[1]戈广双,李子龙,杨凯,马瑞鑫.基于激光扫描测距的机器人粒子滤波定位技术研究[J].传感器与微系统,2017,36(12):36-39. DOI： 10.13873/J.1000-9787(2017)12-0036-04.

GE Guang-shuang, LI Zi-long, YANG Kai, et al. Research on robot particle filtering localization technology based on laser scanning ranging[J]. 2017, 36(12): 36-39. DOI： 10.13873/J.1000-9787(2017)12-0036-04.

摘要

传统的粒子滤波即时定位与地图构建（SLAM）算法在构建地图和目标进行自主定位时

粒子数量大

占用的内存高

重采样之后容易出现粒子匮乏现象

为了提高机器人自主定位的效率

提出了一种改进的重采样策略和粒子更新策略

融入系统模型。在装有机器人操作系统（ROS）的旅行家移动机器人上进行测试

实验结果表明:方法能够有效提升粒子滤波定位的效率。

Abstract

The traditional particle filtering simultaneous localization and mapping（ SLAM） algorithm in the construction of mapping and autonomous positioning

usually need large number of particles and take up a lot of memory

particles appear deprivation after the resampling process

in order to improve the efficiency of robot autonomous positioning

aiming at particle filtering algorithm

propose an improved resampling strategy and particle update strategy

fuse system model. By testing the algorithm on a mobile robot equipped with robot operating system（ ROS）

the algorithm is validated by experimental method. The experimental results show that the method can improve the efficiency of particle filtering positioning.

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references

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