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

作　　者：梁荣光 袁杰[1] 赵瑛瑛 曹学伟 LIANG Rongguang;YUAN Jie;ZHAO Yingying;CAO Xuewei(School of Electrical Engineering,Xinjiang University,Urumqi 830017,China)

机构地区：[1]新疆大学电气工程学院,新疆乌鲁木齐830017

出　　处：《计算机工程与科学》2025年第3期504-512,共9页Computer Engineering & Science

基　　金：国家自然科学基金(62263031);新疆维吾尔自治区自然科学基金(2022D01C53)。

摘　　要：针对视觉SLAM在动态场景中存在数据关联误匹配以及实例分割物体存在误检的问题,提出一种基于改进实例分割的室内动态点特征检测的方法。首先,改进YOLOv7-seg算法,设计了双梯度路径聚合网络D-ELAN和空洞注意力机制DwCBAM,获得当前图像帧中物体准确的轮廓信息。其次,判断动态物体后,从SLAM前端图像帧中剔除动态点特征。最后,利用静态点来构建误差优化模型。实验结果表明:改进后算法相比YOLOv7-seg的mAP平均增加了2.3%。在TUM数据集上,该方法的SLAM绝对轨迹误差相比ORB-SLAM2平均减少95.91%。Addressing the issues that visual SLAM has data association mismatch in dynamic scenarios 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.Secondly,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 experimental 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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]