Cited By
View all- Xu WFan WLi JAlfarraj OTolba AHuang T(2023)A Robust Visual SLAM Method for Additive Manufacturing of Vehicular Parts Under Dynamic ScenesIEEE Access10.1109/ACCESS.2023.325173311(22114-22123)Online publication date: 2023
Fusing Semantic Segmentation and Object Detection for Visual SLAM in Dynamic Scenes
Authors: 
Peilin Yu, Chi Guo, yang Liu, Huyin ZhangAuthors Info & Claims
Peilin Yu, Chi Guo, yang Liu, Huyin ZhangAuthors Info & ClaimsArticle No.: 2, Pages 1 - 7
Abstract
The assumption of static scenes limits the performance of traditional visual SLAM. Many existing solutions adopt deep learning methods or geometric constraints to solve the problem of dynamic scenes, but these schemes are either low efficiency or lack of robustness to a certain extent. In this paper, we propose a solution combining object detection and semantic segmentation to obtain the prior contours of potential dynamic objects. With this prior information, geometric constraints techniques are utilized to assist with removing dynamic feature points. Finally, the evaluation with the public datasets demonstrates that our proposed method can improve the accuracy of pose estimation and robustness of visual SLAM with no efficiency loss in high dynamic scenarios.
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Published In
December 2021
563 pages
ISBN:9781450390927
DOI:10.1145/3489849
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Published: 08 December 2021
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- Research
- Refereed limited
Funding Sources
- Wuhan Science and Technology Planning Application Foundation Frontier Project
- National Key Research and Development Program of China
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VRST '21: 27th ACM Symposium on Virtual Reality Software and Technology
December 8 - 10, 2021
Osaka, Japan
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Overall Acceptance Rate 66 of 254 submissions, 26%
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View all- Xu WFan WLi JAlfarraj OTolba AHuang T(2023)A Robust Visual SLAM Method for Additive Manufacturing of Vehicular Parts Under Dynamic ScenesIEEE Access10.1109/ACCESS.2023.325173311(22114-22123)Online publication date: 2023
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