Research on Visual SLAM Algorithm Based on Semantic Segmentation
LIU Zhenyu
LI Yue
Abstract：At present,most visual SLAM algorithms are based on the assumption of static environment,in which dynamic ob-jects tend to cause inaccurate pose estimation.This paper presents an improved algorithm for dynamic environment.Based on DS-SLAM,the ORB feature points are extracted by adaptive threshold and homogenized by improved quadtree.Then,the sparse optical flow method is used to track the motion of corner points,and the dynamic objects are segmented based on the results of Seg-ment semantics.Finally,geometric constraints are used to filter out dynamic points,and high-quality feature points are reserved for pose estimation to complete positioning and mapping.Compared with DS-SLAM algorithm,the accuracy of the improved algorithm is evaluated by TUM data set,and the real-time performance of the improved algorithm is improved by 9.02%.The camera pose er-ror in dynamic environment is reduced by 38.94%.In this paper,the positioning accuracy and real-time performance of the robot system are improved by improving the quality of feature points and combining the advantages of optical flow method and semantic segmentation.
Keywords：visual SLAM(Simultaneous Localization And Mapping)dynamic environmentsoptical flowsemantic segmen-tation
Publication Date：2024-09-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 2590-2593 )