Survey of object-oriented semantic visual SLAM
TIAN Rui
ZHANG Yun-zhou
YANG Ling-hao
CAO Zhen-zhong
Abstract：Visual simultaneous localization and mapping(VSLAM)is a key technology for autonomous robots,au-tonomous navigation,and AR applications.With the development of deep learning,accurate and efficient semantic infor-mation has been widely used in VSLAM.Compared with traditional SLAM,semantic SLAM leverages semantic informa-tion to improve the accuracy and robustness of localization,and enhances environmental perception ability by object-level reconstruction,which has became the trend in VSLAM research.In this survey,we provide an overview of semantic SLAM techniques with state-of-the-art object SLAM systems.Four key issues of semantic SLAM are summarized,including ob-ject representation,object initialization methods,data association methods,and back-end optimization methods integrating semantic objects.The advantages and disadvantages of the comparison methods are provided.Finally,we propose the future work and challenges of object-level SLAM technology.Currently,semantic SLAM still faces problems such as inaccurate object association and an unified optimization framework has not yet been proposed.How to effectively use and maintain semantic maps for the application of decision and planning tasks,as well as integrate multi-source information to enrich visual perception,will be future research hotspots.
Keywords：visual SLAMdata associationsemantic informationSemantic mapping
Publication Date：2023-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 2160-2171 )