{"title":"Research on Visual SLAM Navigation Techniques for Dynamic Environments","authors":"Tongjun Wang, Peijun Zhao","doi":"10.1155/2023/2025844","DOIUrl":null,"url":null,"abstract":"Synchronous positioning and mapping mainly realize the functions of self-positioning and environment map construction for intelligent navigation technology. In order to solve the problems of low positioning accuracy and poor mapping effect of existing SLAM (simultaneous localization and mapping) systems in indoor dynamic environments and to improve the positioning accuracy, timeliness, and robustness of visual SLAM systems in dynamic environments, an improved visual SLAM method is proposed. Aiming at the inconsistency between the direction of dynamic objects and static background optical flow, this method adopts a high-real-time dynamic region mask detection algorithm to eliminate the feature points in the dynamic region mask, remove the camera motion optical flow according to the original feature information, and then cluster the optical flow amplitude of dynamic objects so as to realize the dynamic region mask detection and eliminate the dynamic signpost points combined with the polar geometric constraints. In order to verify the effectiveness of the improved algorithm, the three evaluation indexes of system accuracy, real-time performance, and the amount of drift are analyzed and verified, respectively, on the TUM dataset. The results show that the proposed algorithm not only has good real-time performance but also improves the accuracy of the system and reduces the amount of drift.","PeriodicalId":50327,"journal":{"name":"International Journal of Distributed Sensor Networks","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Distributed Sensor Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1155/2023/2025844","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Synchronous positioning and mapping mainly realize the functions of self-positioning and environment map construction for intelligent navigation technology. In order to solve the problems of low positioning accuracy and poor mapping effect of existing SLAM (simultaneous localization and mapping) systems in indoor dynamic environments and to improve the positioning accuracy, timeliness, and robustness of visual SLAM systems in dynamic environments, an improved visual SLAM method is proposed. Aiming at the inconsistency between the direction of dynamic objects and static background optical flow, this method adopts a high-real-time dynamic region mask detection algorithm to eliminate the feature points in the dynamic region mask, remove the camera motion optical flow according to the original feature information, and then cluster the optical flow amplitude of dynamic objects so as to realize the dynamic region mask detection and eliminate the dynamic signpost points combined with the polar geometric constraints. In order to verify the effectiveness of the improved algorithm, the three evaluation indexes of system accuracy, real-time performance, and the amount of drift are analyzed and verified, respectively, on the TUM dataset. The results show that the proposed algorithm not only has good real-time performance but also improves the accuracy of the system and reduces the amount of drift.
期刊介绍:
International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.