YGL-SLAM: 动态场景下基于点和线的语义SLAM系统

doi:10.19678/j.issn.1000-3428.0068950

计算机工程 ›› 2025, Vol. 51 ›› Issue (3): 95-104. doi: 10.19678/j.issn.1000-3428.0068950

YGL-SLAM: 动态场景下基于点和线的语义SLAM系统

戴康佳¹, 徐慧英¹^,*(), 朱信忠¹^,², 李悉钰¹, 黄晓³, 陈国强⁴, 张志雄⁵

1. 浙江师范大学计算机科学与技术学院, 浙江金华 321004
2. 北京极智嘉科技股份有限公司, 北京 100012
3. 浙江师范大学教育学院, 浙江金华 321004
4. 浙江航天润博测控技术有限公司, 浙江杭州 311200
5. 浙江工商大学计算机科学与技术学院, 浙江杭州 310018

收稿日期:2023-12-04 出版日期:2025-03-15 发布日期:2025-03-26
通讯作者: 徐慧英
基金资助:
国家自然科学基金(62376252); 浙江省自然科学基金重点项目(LZ22F030003); 国家级大学生创新训练计划重点项目(202310345042)

YGL-SLAM: Point and Line Based Semantic SLAM System for Dynamic Scenes

DAI Kangjia¹, XU Huiying¹^,*(), ZHU Xinzhong¹^,², LI Xiyu¹, HUANG Xiao³, CHEN Guoqiang⁴, ZHANG Zhixiong⁵

1. College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, Zhejiang, China
2. Beijing Geekplus Technology Co., Ltd., Beijing 100012, China
3. College of Education, Zhejiang Normal University, Jinhua 321004, Zhejiang, China
4. Zhejiang Rainbow Aerospace Measurement & Control Technology Co., Ltd., Hangzhou 311200, Zhejiang, China
5. College of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018, Zhejiang, China

Received:2023-12-04 Online:2025-03-15 Published:2025-03-26
Contact: XU Huiying

摘要/Abstract

摘要：

传统的视觉同步定位与建图(SLAM)系统是基于静态环境这一假设的, 然而在现实场景中往往存在动态物体, 这可能导致SLAM位姿估计和地图构建的精度下降、鲁棒性变差, 甚至出现跟踪丢失的情况。针对上述问题, 基于ORB-SLAM2提出新的语义SLAM系统(YGL-SLAM)。该系统首先使用轻量级目标检测算法YOLOv8n追踪动态对象, 获得动态对象的语义信息。然后在跟踪线程的同时提取点特征和线特征, 根据获取的语义信息利用Z-score和对极几何算法剔除动态特征, 以改进SLAM在动态场景中的表现。此外, 鉴于轻量级目标检测算法在追踪动态对象时存在连续帧的漏检测问题, 设计了基于相邻帧的检测补偿方法。在公开数据集TUM和Bonn上的测试结果表明, 相比ORB-SLAM2, YGL-SLAM系统准确率提升超过90%, 对比其他动态SLAM, YGL-SLAM也具有较高的准确度和鲁棒性。

关键词: 动态场景, 语义同步定位与建图, 线特征, 深度学习, YGL-SLAM系统

Abstract:

Traditional vision Simultaneous Localization And Mapping(SLAM) systems are based on the assumption of a static environment. However, real scenes often have dynamic objects, which may lead to decreased accuracy, deterioration of robustness, and even tracking loss in SLAM position estimation and map construction. To address these issues, this study proposes a new semantic SLAM system, named YGL-SLAM, based on ORB -SLAM2. The system first uses a lightweight target detection algorithm named YOLOv8n, to track dynamic objects and obtain their semantic information. Subsequently, both point and line features are extracted from the tracking thread, and the dynamic features are culled based on the acquired semantic information using the Z-score and parapolar geometry algorithms to improve the performance of SLAM in dynamic scenes. Given that lightweight target detection algorithms suffer from missed detection in consecutive frames when tracking dynamic objects, this study designs a detection compensation method based on neighboring frames. Testing on the public datasets TUM and Bonn reveals that YGL-SLAM system improves detection performance by over 90% compared to ORB-SLAM2, while demonstrating superior accuracy and robustness compared to other dynamic SLAM.

Key words: dynamic scenes, semantic Simultaneous Localization And Mapping(SLAM), line features, deep learning, YGL-SLAM system

戴康佳, 徐慧英, 朱信忠, 李悉钰, 黄晓, 陈国强, 张志雄. YGL-SLAM: 动态场景下基于点和线的语义SLAM系统[J]. 计算机工程, 2025, 51(3): 95-104.

DAI Kangjia, XU Huiying, ZHU Xinzhong, LI Xiyu, HUANG Xiao, CHEN Guoqiang, ZHANG Zhixiong. YGL-SLAM: Point and Line Based Semantic SLAM System for Dynamic Scenes[J]. Computer Engineering, 2025, 51(3): 95-104.

https://www.ecice06.com/CN/Y2025/V51/I3/95

图/表 12

图1 YGL-SLAM系统框架

Fig.1 YGL-SLAM system framework

图2 检测补偿结果

Fig.2 Detection compensation results

图3 对极几何约束示意图

Fig.3 Schematic diagram of para-polar geometric constraints

图4 动态特征剔除效果

Fig.4 Effect of dynamic feature removal

图5 线重投影误差

Fig.5 Line reprojection error

图6 ORB-SLAM2、DynaSLAM、YGL-SLAM算法的绝对估计位姿优化

Fig.6 Absolute estimation pose optimization for ORB-SLAM2, DynaSLAM, YGL-SLAM algorithm

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