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RSV-SLAM: Toward Real-Time Semantic Visual SLAM in Indoor Dynamic Environments

Habibpour, M ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-031-47724-9_55
  3. Publisher: 2024
  4. Abstract:
  5. Simultaneous Localization and Mapping (SLAM) plays an important role in many robotics fields, including social robots. Majority of the existing visual SLAM methods rely on a static world assumption and fail in dynamic environments. In this paper, we proposed a real-time semantic RGB-D SLAM system for dynamic environments that is capable of detecting moving objects and maintaining a static map for robust camera tracking. The proposed model is to eliminate the influence of dynamic objects by introducing deep learning-based semantic information to SLAM systems. Furthermore, we augment the semantic segmentation process using an Extended Kalman filter module to detect dynamic objects that are temporarily idle. We have also implemented a generative network to fill in the missing regions of input images belonging to dynamic objects. This highly modular framework has been implemented on the ROS platform and can achieve around 22 fps on a GTX1080. Benchmarking the developed pipeline on dynamic sequences from the TUM dataset suggests that the proposed approach achieves competitive localization error compared to the state-of-the-art methods while operating in near real-time. The source code is publicly available at https://github.com/mobiiin/rsv_slam. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
  6. Keywords:
  7. Mobile Robot ; Visual Simultaneous Localization and Mapping ; Deep learning ; Indoor positioning systems ; Mapping ; Object detection ; Robots ; Semantics ; Dynamic environments ; Dynamic objects ; Localisation Systems ; Mapping method ; Mapping systems ; Real-time semantics ; Semantic segmentation ; Simultaneous localization and mapping ; Social robots ; Kalman filters
  8. Source: Lecture Notes in Networks and Systems ; Volume 823 LNNS , 2024 , Pages 832-844 ; 23673370 (ISSN); 978-303147723-2 (ISBN)
  9. URL: https://megapaper.ir/search/view/book-chapter/10.1007/978-3-031-47724-9_55