Dynamic SLAM with Real-Time Semantic Segmentation and Inter-Frame and Multi-Frame Motion Feature Detection

2024-01-7042

12/13/2024

Features
Event
SAE 2024 Intelligent and Connected Vehicles Symposium
Authors Abstract
Content
Traditional Simultaneous Localization and Mapping (SLAM) methods often assume static environments. This limitation can lead to inaccurate localization or even the loss of tracking in dynamic scenes. To address this issue, we propose a novel SLAM approach specifically designed for dynamic environments. Our method integrates the real-time image semantic segmentation network BisenetV2 with inter-frame and continuous multi-frame motion feature detection. Firstly, semantic segmentation is applied to render the semantic mask, which is then used by the inter-frame motion detection module to identify potential motion features. Subsequently, these suspected motion features are evaluated by a likelihood probability model across consecutive frames. Finally, points with a high probability of motion are monitored in real-time by the Luenberger observer, which filters out motion features and re-adds static ones. Our experiments demonstrate that semantic segmentation can meet real-time requirements in various scenarios. Evaluations of the TUM-RGBD dataset and simulation experiment show that our proposed method significantly improves SLAM accuracy and robustness in dynamic scenes.
Meta TagsDetails
DOI
https://doi.org/10.4271/2024-01-7042
Pages
10
Citation
Qin, X., Gao, C., Huang, S., Zeng, C. et al., "Dynamic SLAM with Real-Time Semantic Segmentation and Inter-Frame and Multi-Frame Motion Feature Detection," SAE Technical Paper 2024-01-7042, 2024, https://doi.org/10.4271/2024-01-7042.
Additional Details
Publisher
Published
Dec 13, 2024
Product Code
2024-01-7042
Content Type
Technical Paper
Language
English