基于位姿图优化的势均衡多伯努利滤波器SLAM方法

张子菁; 章飞

doi:10.11959/j.issn.2096-6652.202305

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基于位姿图优化的势均衡多伯努利滤波器SLAM方法

学术论文 | 更新时间：2024-06-05

- 基于位姿图优化的势均衡多伯努利滤波器SLAM方法
- Cardinalized balanced multi-Bernoulli filter SLAM method based on pose graph optimization
- 智能科学与技术学报 2023年5卷第1期页码：113-120
- 作者机构：
- 作者简介：
  
  [ "张子菁（1995- ），男，江苏科技大学海洋学院硕士生，主要研究方向为移动机器人SLAM方法" ]
  [ "章飞（1979- ），男，博士，江苏科技大学副教授，主要研究方向为水下自主导航、MIMO雷达信号处理、多传感器信息融合" ]
- 基金信息：
  
  国家自然科学基金资助项目;TheNational Natural Science Foundation of China(61801170);国家自然科学基金资助项目;TheNational Natural Science Foundation of China(61801435)
- DOI：10.11959/j.issn.2096-6652.202305
  中图分类号： TP242
- 网络出版日期：2023-03，
  
  纸质出版日期：2023-03-15
- 稿件说明：
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张子菁, 章飞. 基于位姿图优化的势均衡多伯努利滤波器SLAM方法[J]. 智能科学与技术学报, 2023,5(1):113-120.

Zijing ZHANG, Fei ZHANG. Cardinalized balanced multi-Bernoulli filter SLAM method based on pose graph optimization[J]. Chinese journal of intelligent science and technology, 2023, 5(1): 113-120.
张子菁, 章飞. 基于位姿图优化的势均衡多伯努利滤波器SLAM方法[J]. 智能科学与技术学报, 2023,5(1):113-120. DOI： 10.11959/j.issn.2096-6652.202305.

Zijing ZHANG, Fei ZHANG. Cardinalized balanced multi-Bernoulli filter SLAM method based on pose graph optimization[J]. Chinese journal of intelligent science and technology, 2023, 5(1): 113-120. DOI： 10.11959/j.issn.2096-6652.202305.

摘要

在复杂室内环境下，传统基于随机有限集理论的SLAM方法存在机器人位姿精度低、计算量大的问题。针对此问题，提出一种基于位姿图优化的势均衡多伯努利滤波器SLAM方法。首先，该方法通过势均衡多伯努利滤波器获得地图特征估计，避免了数据关联。其次，提出了自适应信息控制法，丰富先验信息。然后，通过自适应信息控制法将位姿图优化理论与势均衡多伯努利滤波器SLAM结合，优化机器人的位姿估计。最后，进行实验对比分析，结果表明所提方法比RB-PHD-SLAM方法有更好的SLAM精度及实时性。

Abstract

In the complex indoor environment

the traditional SLAM method based on random finite set theory has the problems of low robot pose accuracy and large amount of calculation.To solve these problems

a cardinalized balanced multi-Bernoulli filter SLAM method based on pose graph optimization was proposed.First of all

the cardinalized balanced multi-Bernoulli filter was used to estimate the map features

which avoided data association.What is more

an adaptive information control method was proposed to enrich the prior information.Then

the pose graph optimization theory was combined with cardinalized balanced multi-Bernoulli filter SLAM through adaptive information control method to optimize the pose estimation of the robot.Finally

through experimental comparative analysis

the results show that this method have better SLAM accuracy and real-time performance than the RB-PHD-SLAM method.

关键词

Keywords

references

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