低纹理环境下融合点线面特征的双目视觉SLAM算法

doi:10.3785/j.issn.1008-973X.2026.02.010

浙江大学学报(工学版)

2026, Vol. 60

Issue (2): 322-331 DOI: 10.3785/j.issn.1008-973X.2026.02.010

计算机技术与控制工程

低纹理环境下融合点线面特征的双目视觉SLAM算法

汪泽1(

),饶蕾1,*(

),范光宇1,陈年生1,程松林1,杨定裕2,姜楚乔1

1. 上海电机学院电子信息学院，上海 201306
2. 浙江大学区块链与数据安全全国重点实验室，浙江杭州 310058

Stereo visual SLAM algorithm for fusing point-line-plane features in low texture environments

Ze WANG1(

),Lei RAO1,*(

),Guangyu FAN1,Niansheng CHEN1,Songlin CHENG1,Dingyu YANG2,Chuqiao JIANG1

1. School of Electronic Information Engineering, Shanghai Dianji University, Shanghai 201306, China
2. State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou 310058, China

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摘要：

针对机器人在低纹理场景下基于点特征的ORB-SLAM2存在定位精度低、轨迹漂移误差较大的问题，提出融合点线面特征的双目视觉SLAM算法. 在ORB-SLAM2中设计并引入改进的EDLines线特征提取算法，通过短线抑制和相似直线合并策略，降低计算时间并提高线特征提取的质量. 提出基于相交直线的平面特征提取方法，基于所提取面特征的几何约束优化位姿估计，减少重投影误差. 提出点线面特征的联合优化方法，融合多种特征的几何关系，减少由单一特征带来的误差累积. 在KITTI、EuRoC和UMA-VI数据集下测试所提算法的有效性. 实验结果表明，相较于ORB-SLAM2、点线特征SLAM以及点面特征SLAM算法，所提算法在定位精度与鲁棒性方面更优.

关键词： 低纹理环境; 视觉SLAM; 线特征; 面特征; 联合优化

Abstract:

To address the issues of low positioning accuracy and significant trajectory drift errors in ORB-SLAM2 based on point features under low-texture scenes, a stereo visual SLAM algorithm integrating point, line, and plane features was proposed. An improved EDLines line feature extraction algorithm was designed and introduced into ORB-SLAM2. By employing short-line suppression and similar-line merging strategies, computational time was reduced while enhancing the quality of line feature extraction. A plane features extraction method based on intersecting lines was proposed. Geometric constraints derived from extracted plane features were used to optimize pose estimation, and reprojection errors were reduced. A joint optimization method for point, line, and plane features was introduced, integrating geometric relationships across multiple features to mitigate cumulative errors from single-feature reliance. The proposed algorithm’s effectiveness was validated on KITTI, EuRoC, and UMA-VI datasets. Experimental results demonstrate that compared to ORB-SLAM2, point-line feature-based SLAM, and point-plane feature-based SLAM algorithms, the proposed method achieves superior positioning accuracy and robustness.

Key words: low-texture environment visual SLAM line features plane features joint optimization

收稿日期: 2025-02-15 出版日期: 2026-02-03

CLC:

TP 242

基金资助: 国家自然科学基金资助项目（61702320）；上海市晨光计划（15CG62）.

通讯作者: 饶蕾 E-mail: 949795713@qq.com;raol@sdju.edu.cn

作者简介: 汪泽（1997—），男，硕士生，从事视觉SLAM算法研究. orcid.org/0009-0007-2603-3803. E-mail：949795713@qq.com

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引用本文:

汪泽,饶蕾,范光宇,陈年生,程松林,杨定裕,姜楚乔. 低纹理环境下融合点线面特征的双目视觉SLAM算法[J]. 浙江大学学报(工学版), 2026, 60(2): 322-331.

Ze WANG,Lei RAO,Guangyu FAN,Niansheng CHEN,Songlin CHENG,Dingyu YANG,Chuqiao JIANG. Stereo visual SLAM algorithm for fusing point-line-plane features in low texture environments. Journal of ZheJiang University (Engineering Science), 2026, 60(2): 322-331.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2026.02.010 或 https://www.zjujournals.com/eng/CN/Y2026/V60/I2/322

图 1 融合点线面特征的双目视觉SLAM算法框架

图 2 相似线段合并

图 3 不同算法的线特征提取效果对比

图 4 线段及端点匹配

图 5 平面特征提取

表 1 不同场景的点线面特征数量

图 6 特征提取

表 2 不同视觉SLAM算法在KITTI 数据集上的平移均方根误差比较

图 7 不同视觉SLAM算法在KITTI数据集2种序列中的位姿估计轨迹对比

表 3 不同视觉SLAM算法在EuRoC数据集上的平移均方根误差比较

图 8 不同视觉SLAM算法在序列V2_01_easy中的位姿估计轨迹对比

图 9 序列V1_02_medium的特征地图

表 4 不同视觉SLAM算法在UMA-VI数据集上的平移均方根误差比较

图 10 不同视觉SLAM算法在序列corridor-eng中的位姿估计轨迹比较

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