基于分组策略的点线特征融合同步定位与地图构建算法  

Simultaneous Localization and Mapping Algorithm with Point and Line Features Based on Grouping Strategy

作　　者：方琪 王晓华[1] 苏杰 Fang Qi;Wang Xiaohua;Su Jie(School of Electronics and Information,Xi’an Polytechnic University,Xi'an,Shaanxi 710048,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《激光与光电子学进展》2021年第14期397-405,共9页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(51905405);陕西省自然科学基础研究计划(2019JQ-855);西安工程大学2020年度研究生创新基金(chx2020013);大学生创新创业训练项目(S202010709038)。

摘　　要：针对视觉同步定位与地图构建(SLAM)算法中线特征提取稳健性差、匹配效率低的问题,提出了一种基于分组策略的点线特征融合视觉SLAM算法。首先,通过引入线段分组策略合并潜在的同源线段、生成高质量长线段,并利用公共结点筛选出有效的线特征。然后,根据点线特征的仿射不变性直接匹配线特征。最后,构建点线融合特征的误差函数,使重投影误差最小化,进而提高相机位姿的估计精度。在KITTI、EuRoC、TUM以及YorkUrban数据集上的实验结果表明,本算法可以有效提升特征提取的稳健性,进而提高相机位姿估计与建图的精度。This paper presents a visual simultaneous localization and mapping(SLAM)algorithm that combines the point and line features using agrouping strategy.The algorithm aims to improve the poor robustness of line feature extraction and low matching efficiency in the visual SLAM algorithm.First,the line segments grouping strategy merges potential homologous line segments to generate high-quality long line segments using the common junctions to screen the effective line features.Then,these line features are matched according to the affine invariance of point and line features.Finally,the error function of the point and line fusion features is constructed to minimize the reprojection error,thereby improving the estimation accuracy of the camera pose.When experimentally tested on the KITTI,EuRoC,TUM,and YorkUrban datasets,the algorithm effectively improved the robustness of feature extraction,thus improving the accuracy of camera pose estimation and mapping.

关 键 词：机器视觉 同步定位与地图构建 分组策略 公共结点 仿射不变性 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]