摘要
为提升室内机器人在动态场景中的定位精度,同时构建细节丰富的三维语义地图,提出一种基于特征融合及动态背景去除的室内机器人语义VI-SLAM(visual-inertial simultaneous localization and mapping)算法。首先,改进ORB-SLAM3算法框架,设计一种可以实时构建三维稠密点云地图的VI-SLAM算法;其次,将目标识别算法YOLOv5与VI-SLAM算法融合,获取二维语义信息,结合二维语义信息与极线约束原理去除动态特征;再次,将二维语义信息映射为三维语义标签,将语义特征与点云特征相融合,构建三维语义地图;最后,基于公开数据集及移动机器人平台,在动态场景下开展三维语义地图构建实验。实验结果验证了提出的该语义VISLAM算法在动态环境下定位与建图的可行性和有效性。
An indoor robot semantic VI-SLAM algorithm based on feature fusion and dynamic background removal is proposed to improve the positioning accuracy of indoor robots in dynamic scenes and build a three-dimensional(3D)semantic map with rich details.The framework of the ORB-SLAM3 algorithm is improved,and a VI-SLAM algorithm for real-time construction of 3D dense point cloud maps is designed.The algorithm fuses target recognition algorithms YOLOv5 and VI-SLAM to obtain two-dimensional(2D)semantic information.Dynamic features are then removed by combining the 2D semantic information with the epipolar constraint principle.Subsequently,the 2D semantic information is mapped into a 3D semantic tag,constructing a 3D semantic map by fusing the semantic features with the pointcloud features.Finally,experiments in 3D semantic map construction were conducted in indoor scenes using public data sets and a mobile robot platform.Results verify the feasibility and effectiveness of the semantic VI-SLAM algorithm in dynamic environments.
作者
王立鹏
王小晨
齐尧
张佳鹏
WANG Lipeng;WANG Xiaochen;QI Yao;ZHANG Jiapeng(College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《智能系统学报》
CSCD
北大核心
2024年第6期1438-1448,共11页
CAAI Transactions on Intelligent Systems
基金
黑龙江省教育科学规划2023年度重点课题(GJB1423059)
国家自然科学基金项目(62173103)
黑龙江省自然科学基金项目(LH2024F037)
中央高校基本科研业务费专项(3072024XX0403).
