Binocular vision SLAM with fused point and line features in weak texture environment

GONG Kun; XIN Xu; CHEN Xiaoqing; XU Yuelei; ZHANG Zhaoxiang

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Binocular vision SLAM with fused point and line features in weak texture environment

更新时间：2024-01-23

- Binocular vision SLAM with fused point and line features in weak texture environment
- Optics and Precision Engineering Pages: 1-12(2024)
- 作者机构：
  
  1.西北工业大学无人系统技术研究院，陕西西安 710072
  2.中国人民解放军军事科学院国防科技创新研究院，北京 100850
- 作者简介：
  
  Email： xuyuelei@nwpu.edu.cn
- 基金信息：
- DOI：
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龚坤,徐鑫,陈小庆等.弱纹理环境下融合点线特征的双目视觉SLAM[J].光学精密工程,

GONG Kun,Xu XIN,CHEN Xiaoqing,et al.Binocular vision SLAM with fused point and line features in weak texture environment[J].Optics and Precision Engineering,
龚坤,徐鑫,陈小庆等.弱纹理环境下融合点线特征的双目视觉SLAM[J].光学精密工程, DOI：10.37188/OPE.XXXXXXXX.0001

GONG Kun,Xu XIN,CHEN Xiaoqing,et al.Binocular vision SLAM with fused point and line features in weak texture environment[J].Optics and Precision Engineering, DOI：10.37188/OPE.XXXXXXXX.0001

摘要

针对室内弱纹理环境下基于点特征的视觉SLAM（Simultaneous Localization and Mapping）存在的轨迹漂移等问题，本文提出了一种融合点线特征的双目视觉SLAM系统，并对线特征的提取与匹配问题展开研究。为了提高线特征的质量，本文通过长度与梯度抑制、短线合并等方法，进一步改进了LSD（Line Segment Detector）线特征提取方法。同时，通过将匹配问题转换为优化问题，并利用几何约束构建代价函数，提出了一种基于几何约束的快速线段三角化方法。实验结果表明，本文所提方法在多个数据集上的表现都优于基于描述子的传统方法，尤其在室内弱纹理场景下，其平均匹配精度达到91.67%，平均匹配时间仅需7.4ms。基于此方法，本文提出的双目视觉SLAM系统在弱纹理数据集上与已有算法ORBSLAM2，PL-SLAM的定位误差分别为1.24 m，7.49 m，3.67 m，定位精度优于现有算法。

Abstract

In response to the issues of trajectory drift in visual Simultaneous Localization and Mapping （SLAM） based on point features in indoor environments with weak textures， this paper proposes a binocular visual SLAM system that integrates point and line features. The paper focuses on the extraction and matching of line features in binocular visual SLAM. To enhance the quality of line features， an improved line feature extraction method based on the Line Segment Detector （LSD） algorithm is introduced， incorporating techniques such as length and gradient suppression， as well as short line merging. Moreover， the matching problem is transformed into an optimization problem， where a cost function is constructed based on geometric constraints. A fast line segment triangulation method using L1-norm sparse solution is presented to efficiently perform line matching and triangulation computations. Experimental results demonstrate that the proposed method outperforms traditional descriptor-based methods on multiple datasets， particularly in indoor environments with weak textures， achieving an average matching accuracy of 91.67% with an average matching time of only 7.4ms. Utilizing this method， the binocular visual SLAM system proposed in this paper achieves positioning errors of 1.24m， 7.49m， and 3.67m on weak texture datasets， surpassing existing algorithms such as ORBSLAM2 and PL-SLAM in terms of positioning accuracy.

关键词

双目视觉线特征提取视觉SLAM特征匹配

Keywords

binocular visionline featuresvision SLAMfeature matching

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