RDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods

The scene rigidity is a strong assumption in typical visual Simultaneous Localization and Mapping (vSLAM) algorithms. Such strong assumption limits the usage of most vSLAM in dynamic real-world environments, which are the target of several relevant applications such as augmented reality, semantic ma...

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Vydáno v:IEEE access Ročník 9; s. 23772 - 23785
Hlavní autoři: Liu, Yubao, Miura, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Augmented reality
Cameras
Data analysis
Dynamic SLAM
Heuristic algorithms
Mask R-CNN
Object recognition
Optimization
ORB SLAM
Outliers (statistics)
Real time
Real-time systems
Robotics
SegNet
Semantic segmentation
Semantics
Simultaneous localization and mapping
Source code
Tracking
Vehicle dynamics
Visualization
ISSN:2169-3536, 2169-3536
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Shrnutí:The scene rigidity is a strong assumption in typical visual Simultaneous Localization and Mapping (vSLAM) algorithms. Such strong assumption limits the usage of most vSLAM in dynamic real-world environments, which are the target of several relevant applications such as augmented reality, semantic mapping, unmanned autonomous vehicles, and service robotics. Many solutions are proposed that use different kinds of semantic segmentation methods (e.g., Mask R-CNN, SegNet) to detect dynamic objects and remove outliers. However, as far as we know, such kind of methods wait for the semantic results in the tracking thread in their architecture, and the processing time depends on the segmentation methods used. In this paper, we present RDS-SLAM, a real-time visual dynamic SLAM algorithm that is built on ORB-SLAM3 and adds a semantic thread and a semantic-based optimization thread for robust tracking and mapping in dynamic environments in real-time. These novel threads run in parallel with the others, and therefore the tracking thread does not need to wait for the semantic information anymore. Besides, we propose an algorithm to obtain as the latest semantic information as possible, thereby making it possible to use segmentation methods with different speeds in a uniform way. We update and propagate semantic information using the moving probability, which is saved in the map and used to remove outliers from tracking using a data association algorithm. Finally, we evaluate the tracking accuracy and real-time performance using the public TUM RGB-D datasets and Kinect camera in dynamic indoor scenarios. Source code and demo: https://github.com/yubaoliu/RDS-SLAM.git
Bibliografie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3050617