Fast PL-SLAM Algorithm Based on Improved Keyframe Extraction Strategy

CHEN Meng-yuan; DING Ling-mei; ZHANG Yu-kun

doi:10.12263/DZXB.20210567

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Fast PL-SLAM Algorithm Based on Improved Keyframe Extraction Strategy

PAPERS | 更新时间：2025-12-11

- Fast PL-SLAM Algorithm Based on Improved Keyframe Extraction Strategy
- ACTA ELECTRONICA SINICA Vol. 50, Issue 3, Pages: 608-618(2022)
- 作者机构：
  
  1.安徽工程大学电气工程学院, 安徽芜湖 241000
  2.高端装备先进感知与智能控制教育部重点实验室，安徽芜湖 241000
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210567
  CLC： TP242.6
- Received：06 May 2021，
  
  Revised：2021-06-10，
  
  Published：25 March 2022
- 稿件说明：
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陈孟元,丁陵梅,张玉坤.基于改进关键帧选取策略的快速PL-SLAM算法[J].电子学报,2022,50(03):608-618.

CHEN Meng-yuan,DING Ling-mei,ZHANG Yu-kun.Fast PL-SLAM Algorithm Based on Improved Keyframe Extraction Strategy[J].ACTA ELECTRONICA SINICA,2022,50(03):608-618.
陈孟元,丁陵梅,张玉坤.基于改进关键帧选取策略的快速PL-SLAM算法[J].电子学报,2022,50(03):608-618. DOI： 10.12263/DZXB.20210567.

CHEN Meng-yuan,DING Ling-mei,ZHANG Yu-kun.Fast PL-SLAM Algorithm Based on Improved Keyframe Extraction Strategy[J].ACTA ELECTRONICA SINICA,2022,50(03):608-618. DOI： 10.12263/DZXB.20210567.

摘要

针对PL-SLAM（Point and Line Simultaneous Localization And Mapping）算法在稠密场景下同时使用点线特征造成特征计算冗余，以及曲线运动时漏选关键帧等问题，提出一种基于改进关键帧选取策略的快速PL-SLAM算法（Improved keyframe extraction strategy-based Fast PL-SLAM algorithm， IFPL-SLAM）.该算法引入一种基于信息熵引导的位姿跟踪决策，使用信息熵评价优先提取的特征点，依据评价结果决策点线特征的融合使用方式，避免了在纹理稠密场景下点线特征同时使用造成数据冗余，提高了算法的实时性;与此同时，为避免曲线运动时漏选关键帧，采用逆向索引关键帧选取策略补充在曲线运动中漏选的关键帧，提高了闭环的准确率和定位精度.在公开的KITTI数据集和TUM数据集中进行测试，测试结果表明本文算法的运行时间与PL-SLAM算法相比减少了16.0%，绝对轨迹误差相比于PL-SLAM算法缩小了23.4%，表现出了良好的构图能力.

Abstract

To address the problems that PL-SLAM(Point and Line Simultaneous Localization And Mapping) algorithm uses point and line features at the same time in dense scenes

which cause redundancy in feature calculation

and misses keyframe selection during curve motion

an IFPL-SLAM(Improved keyframe extraction strategy-based Fast PL-SLAM) algorithm is proposed. The algorithm introduces an information entropy-guided bit tracking decision

evaluates the priority extracted feature points using information entropy

and decides the fusion of point and line features based on the evaluation results

which avoids data redundancy caused by the simultaneous use of point and line features in dense texture scenes and improves the real-time performance of the algorithm. At the same time

in order to avoid missing keyframes during curve motion

the inverse index keyframe selection strategy is used to supplement the keyframes missed during curve motion

which improves the accuracy and positioning precision of the closed loop. The test results on the publicly available KITTI dataset and TUM dataset show that the running time of the algorithm proposed is reduced by 16.0% compared to the PL-SLAM algorithm

and the localization accuracy is increased by 23.4% compared to PL-SLAM

which exhibits a good mapping capability.

关键词

Keywords

references

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