计算机工程与科学2025，Vol.47Issue(3)：504-512,9.DOI:10.3969/j.issn.1007-130X.2025.03.012
基于改进实例分割的室内动态视觉SLAM方法
A visual SLAM method based on improved instance segmentation for indoor dynamic scenes
摘要
Abstract
Addressing the issues that visual SLAM has data association mismatch in dynamic scenari-os and false detection in instance segmentation,an indoor dynamic point feature detection method based on improved instance segmentation is proposed.Firstly,the YOLOv7-seg algorithm is improved,and a double gradient path aggregation network(D-ELAN)and a hole attention mechanism(DwCBAM)are designed to obtain the accurate contour information of dynamic objects in the current image frame.Sec-ondly,dynamic feature points are eliminated from the SLAM front-end image frames after determining the object class.Finally,static points are utilized to construct an error optimization model.The experi-mental results show that the improved algorithm increases the mAP by 2.3％on average compared to YOLOv7-seg.On the TUM dataset,the method reduces the SLAM absolute trajectory error by 95.91％on average compared to ORB-SLAM2.关键词
视觉SLAM/实例分割/动态剔除/位姿估计Key words
visual SLAM/instance segmentation/dynamic reject/pose estimation分类
信息技术与安全科学引用本文复制引用
梁荣光,袁杰,赵瑛瑛,曹学伟..基于改进实例分割的室内动态视觉SLAM方法[J].计算机工程与科学,2025,47(3):504-512,9.基金项目
国家自然科学基金(62263031) （62263031）
新疆维吾尔自治区自然科学基金(2022D01C53) （2022D01C53）
