针对室内弱纹理环境下基于点特征的视觉同步定位与建图(Simultaneous Localization and Mapping,SLAM)存在的轨迹漂移等问题,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,通过长度与梯度抑制、短线合并等方法,进一步改进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7.4 ms.基于此方法,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1.24,7.49,3.67 m,定位精度优于现有算法.
Binocular vision SLAM with fused point and line features in weak texture environment
Addressing the challenge of trajectory drift in visual Simultaneous Localization and Mapping(SLAM)due to point features in texture-deficient indoor settings,this study introduces a binocular visual SLAM system that combines point and line features.It emphasizes the extraction and matching of line fea-tures within binocular visual SLAM.An enhanced line feature extraction technique,based on the Line Seg-ment Detector(LSD)algorithm,is proposed.This includes improvements like length and gradient filter-ing,and the amalgamation of short lines.Additionally,the matching issue is redefined as an optimization challenge,creating a cost function based on geometric constraints.A novel,efficient line segment triangu-lation approach,leveraging the L1-norm sparse solution,is developed for effective line matching and trian-gulation.Experimental evidence shows that our method surpasses traditional descriptor-based approaches across various datasets,especially in texture-sparse indoor areas,achieving a remarkable average matching accuracy of 91.67%and a swift average matching time of 7.4 ms.Employing this technique,our binocular visual SLAM system records positioning errors of 1.24,7.49,and 3.67 m on texture-sparse datasets,out-performing leading algorithms like ORBSLAM2 and PL-SLAM in positioning precision.
binocular visionline featuresvision simultaneous localization and mappingfeature matching
万方数据
维普
