Fusing semantics and motion state detection for robust visual SLAM
Achieving robust pose tracking and mapping in highly dynamic environments is a major challenge faced by existing visual SLAM (vSLAM) systems. In this paper, we increase the robustness of existing vSLAM by accurately removing moving objects from the scene so that they will not contribute to pose esti...
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sg-ntu-dr.10356-1785882024-06-27T01:28:56Z Fusing semantics and motion state detection for robust visual SLAM Singh, Gaurav Wu, Meiqing Lam, Siew-Kei College of Computing and Data Science School of Computer Science and Engineering 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) Computer and Information Science Semantics Robustness Achieving robust pose tracking and mapping in highly dynamic environments is a major challenge faced by existing visual SLAM (vSLAM) systems. In this paper, we increase the robustness of existing vSLAM by accurately removing moving objects from the scene so that they will not contribute to pose estimation and mapping. Specifically, semantic information is fused with motion states of the scene via a probability framework to enable accurate and robust moving object extraction in order to retain the useful features for pose estimation and mapping. Our work highlights the importance of distinguishing between motion states of potential moving objects for vSLAM in highly dynamic environments. The proposed method can be integrated into existing vSLAM systems to increase their robustness in dynamic environments without incurring much computation cost. We provide extensive experimental results on three well-known datasets to show that the proposed technique outperforms existing vSLAM methods in indoor and outdoor environments, under various scenarios such as crowded scenes. 2024-06-27T01:28:56Z 2024-06-27T01:28:56Z 2020 Conference Paper Singh, G., Wu, M. & Lam, S. (2020). Fusing semantics and motion state detection for robust visual SLAM. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2753-2762. https://dx.doi.org/10.1109/WACV45572.2020.9093359 978-1-7281-6553-0 https://hdl.handle.net/10356/178588 10.1109/WACV45572.2020.9093359 2-s2.0-85085519132 2753 2762 en © 2020 IEEE. All rights reserved. |
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Computer and Information Science Semantics Robustness Singh, Gaurav Wu, Meiqing Lam, Siew-Kei Fusing semantics and motion state detection for robust visual SLAM |
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Achieving robust pose tracking and mapping in highly dynamic environments is a major challenge faced by existing visual SLAM (vSLAM) systems. In this paper, we increase the robustness of existing vSLAM by accurately removing moving objects from the scene so that they will not contribute to pose estimation and mapping. Specifically, semantic information is fused with motion states of the scene via a probability framework to enable accurate and robust moving object extraction in order to retain the useful features for pose estimation and mapping. Our work highlights the importance of distinguishing between motion states of potential moving objects for vSLAM in highly dynamic environments. The proposed method can be integrated into existing vSLAM systems to increase their robustness in dynamic environments without incurring much computation cost. We provide extensive experimental results on three well-known datasets to show that the proposed technique outperforms existing vSLAM methods in indoor and outdoor environments, under various scenarios such as crowded scenes. |
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College of Computing and Data Science |
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College of Computing and Data Science Singh, Gaurav Wu, Meiqing Lam, Siew-Kei |
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Conference or Workshop Item |
author |
Singh, Gaurav Wu, Meiqing Lam, Siew-Kei |
author_sort |
Singh, Gaurav |
title |
Fusing semantics and motion state detection for robust visual SLAM |
title_short |
Fusing semantics and motion state detection for robust visual SLAM |
title_full |
Fusing semantics and motion state detection for robust visual SLAM |
title_fullStr |
Fusing semantics and motion state detection for robust visual SLAM |
title_full_unstemmed |
Fusing semantics and motion state detection for robust visual SLAM |
title_sort |
fusing semantics and motion state detection for robust visual slam |
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2024 |
url |
https://hdl.handle.net/10356/178588 |
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1806059756830326784 |