Visual SLAM Based on Object Detection Network:A Review

作     者：Jiansheng Peng Dunhua Chen Qing Yang Chengjun Yang Yong Xu Yong Qin 

作者机构：College of AutomationGuangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and ManufacturingHechi UniversityHechi547000China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第77卷第12期

页      面：3209-3236页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Science Foundation of China(No.62063006) to the Natural Science Foundation of Guangxi Province(No.2023GXNS-FAA026025) to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2021RYC06005) to the Research Project for Young and Middle-aged Teachers in Guangxi Universities(ID:2020KY15013) to the Special Research Project of Hechi University(ID:2021GCC028) supported by the Project of Outstanding Thousand Young Teachers’Training in Higher Education Institutions of Guangxi,Guangxi Colleges and Universities Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region. 

主　　题：Object detection visual SLAM visual odometry loop closure detection semantic map 

摘      要：Visual simultaneous localization and mapping(SLAM)is crucial in robotics and autonomous driving.However,traditional visual SLAM faces challenges in dynamic environments.To address this issue,researchers have proposed semantic SLAM,which combines object detection,semantic segmentation,instance segmentation,and visual SLAM.Despite the growing body of literature on semantic SLAM,there is currently a lack of comprehensive research on the integration of object detection and visual SLAM.Therefore,this study aims to gather information from multiple databases and review relevant literature using specific keywords.It focuses on visual SLAM based on object detection,covering different aspects.Firstly,it discusses the current research status and challenges in this field,highlighting methods for incorporating semantic information from object detection networks into mileage measurement,closed-loop detection,and map construction.It also compares the characteristics and performance of various visual SLAM object detection algorithms.Lastly,it provides an outlook on future research directions and emerging trends in visual SLAM.Research has shown that visual SLAM based on object detection has significant improvements compared to traditional SLAM in dynamic point removal,data association,point cloud segmentation,and other technologies.It can improve the robustness and accuracy of the entire SLAM system and can run in real time.With the continuous optimization of algorithms and the improvement of hardware level,object visual SLAM has great potential for development.