基于局部语义拓扑图的视觉SLAM闭环检测

doi:10.13973/j.cnki.robot.190005

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张括嘉, 张云洲, 吕光浩, 龚益群. 基于局部语义拓扑图的视觉SLAM闭环检测[J]. 机器人, 2019, 41(5): 649-659.DOI: 10.13973/j.cnki.robot.190005. ZHANG Kuojia, ZHANG Yunzhou, Lü Guanghao, GONG Yiqun. Loop Closure Detection Based on Local Semantic Topology for Visual SLAM System. ROBOT, 2019, 41(5): 649-659. DOI: 10.13973/j.cnki.robot.190005.

摘要针对视觉SLAM（同步定位与地图创建）中现有的闭环检测方法容易产生假阳性检测的问题，利用YOLOv3目标检测算法获取场景中的语义信息，以DBSCAN（density-based spatial clustering of application with noise）算法修正错误检测和遗漏检测，构建语义节点，对关键帧形成局部语义拓扑图．利用图像特征和目标类别信息进行语义节点匹配，计算不同语义拓扑图中对应边的变换关系，得到关键帧之间的相似度，并根据连续关键帧的相似度变化情况进行闭环的判断．在公开数据集上的实验表明，目标聚类有效地提高了室内场景下的闭环检测准确性．与单纯利用传统视觉特征的算法相比，本文算法能够获得更加准确的闭环检测结果．

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关键词 ：移动机器人, 视觉SLAM, 闭环检测, 语义信息, 局部语义拓扑图

Abstract：Aiming at the problem of false positive detection caused by current loop closure detection methods in visual SLAM (simultaneous localization and mapping), YOLOv3 (you look only once v3) object detection algorithm is adopted to obtain semantic information in scenes. Wrong and missing object detection are corrected by DBSCAN (density-based spatial clustering of application with noise) algorithm to create semantic nodes, which are then used to construct local semantic topology for a keyframe. After matching semantic nodes based on visual features and classification information of objects, transforming relationship can be computed for corresponding edges in different semantic topologies, which can obtain a similarity score. Judgement of loop closure is performed according to the changes of similarities between consequent keyframes. Experiments on benchmark datasets prove that object clustering effectively improves the accuracy of loop closure detection in indoor scenes. Compared with algorithms which are barely based on traditional visual features, the proposed algorithm can achieve loop closure detection with a higher accuracy.

Key words： mobile robot visual SLAM (simultaneous localization and mapping) loop closure detection semantic information local semantic topology

收稿日期: 2019-01-02 录用日期: 2019-06-10

TP242

基金资助:国家重点研发计划（2017YFC0805005）；辽宁省自然科学基金（20180520040）；十三五装备预研共用技术和领域基金（41412050202）；中央高校基本科研业务费专项资金（N172608005，N182608004）；国家自然科学基金（61471110）

通讯作者: 张云洲,zhangyunzhou@mail.neu.edu.cn E-mail: zhangyunzhou@mail.neu.edu.cn

作者简介: 张括嘉(1996-),男,硕士生.研究领域:视觉SLAM,机器学习.
张云洲(1974-),男,博士,教授.研究领域:智能机器人,计算机视觉.
吕光浩(1995-),男,硕士生.研究领域:视觉SLAM.

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https://robot.sia.cn/CN/10.13973/j.cnki.robot.190005 或 https://robot.sia.cn/CN/Y2019/V41/I5/649

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