融合点线特征的视觉惯性SLAM算法  

Visual inertial SLAM algorithm fusing point and line feature

作　　者：付煜 郑爽 别桐 朱晓庆 王全民[1] Fu Yu;Zheng Shuang;Bie Tong;Zhu Xiaoqing;Wang Quanmin(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence&Intelligent System,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124 [2]北京工业大学,计算智能与智能系统北京市重点实验室,北京100124

出　　处：《计算机应用研究》2022年第2期349-355,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61773027);北京市自然科学基金资助项目(4202005,4202004);北京市教育委员会科技计划一般项目(KM201810005028)。

摘　　要：为使移动机器人在昏暗场景中获得更高的定位精度以实现更好的建图效果,针对特征点稀疏、相机移动过快所导致的SLAM算法初始化困难、定位精度较低等问题,提出了一种融合点线特征的视觉惯性SLAM算法。通过EDLines线特征提取匹配算法来引入丰富的线特征,进而提升特征跟踪的鲁棒性;利用惯性传感器和视觉传感器的互补优势,通过视觉惯性分步联合初始化方法在初始化阶段分三步进行优化,进而提升初始化的精度和速度。实验表明,该算法所使用的线特征提取匹配算法相比传统的LSD算法具有了更快的匹配速度和更低的误匹配率,并且该算法在视觉惯性数据集中相机位姿的估计精度更高、鲁棒性更强。To solve the problems of difficult initialization and low positioning accuracy of SLAM algorithm caused by sparse feature points and fast camera movement in practical application scenes,this paper proposed a visual-inertial SLAM algorithm.It used EDLines feature extraction and matching algorithm to introduce rich line features to improve the robustness of feature tracking.Using the complementary advantages of the inertial sensor and the visual sensor,it carried out the optimization in three steps in the initial stage through the visual-inertial initialization by steps method,so as to improve the accuracy and speed of the initialization.Experiments show that the line feature extraction and matching algorithm used in this algorithm provides faster matching speed and lower mismatching rate than the traditional LSD algorithm.Moreover,the proposed algorithm has higher accuracy and stronger robustness in the estimation of camera pose in the visual-inertial data set.

关 键 词：视觉同时定位与地图构建 视觉惯性融合 稀疏建图 点线特征提取 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]