A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

Miao, Sheng; Liu, Xiaoxiong; Wei, Dazheng; Li, Changze

doi:10.3390/ijgi10100673

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…In computer vision and 3D reconstruction, it is commonly assumed that the reprojection error follows a Gaussian distribution [27,28]. Based on this assumption, the probability of a feature point's movement can be calculated using the reprojection error.…”

Section: I+1 Wmentioning

confidence: 99%

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

Wang,

Liu,

Zhao

et al. 2024

IJGI

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A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance.

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Section: I+1 Wmentioning

confidence: 99%

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

Wang,

Liu,

Zhao

et al. 2024

IJGI

Self Cite

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“…Feng Li et al [18] used an enhanced semantic segmentation network to detect and segment dynamic features of dynamic SLAM environments as a way to achieve dynamic feature rejection. Miao Sheng et al [19] adopted a static and dynamic image segmentation method to improve the accuracy of image construction to reduce the interference of dynamic objects in image construction. This method uses the semantic module to segment the image and then uses the geometric module to detect and eliminate the dynamic objects.…”

Section: Introductionmentioning

confidence: 99%

Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes

Zhang

Wang

2022

Applied Sciences

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When building a map of a dynamic environment, simultaneous localization and mapping systems have problems such as poor robustness and inaccurate pose estimation. This paper proposes a new mapping method based on the ORB-SLAM2 algorithm combined with the YOLOv5 network. First, the YOLOv5 network of the tracing thread is used to detect dynamic objects of each frame, and to get keyframes with detection of dynamic information. Second, the dynamic objects of each image frame are detected using the YOLOv5 network, and the detected dynamic points are rejected. Finally, the global map is constructed using the keyframes after eliminating the highly dynamic objects. The test results using the TUM dataset show that when the map is constructed in a dynamic environment, compared with the ORB-SLAM2 algorithm, the absolute trajectory error of our algorithm is reduced by 97.8%, and the relative positional error is reduced by 59.7%. The average time consumed to track each image frame is improved by 94.7% compared to DynaSLAM. In terms of algorithmic real-time performance, this paper’s algorithm is significantly better than the comparable dynamic SLAM map-building algorithm DynaSLAM.

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Dynamic Object-Aware SLAM: YoloV8 Integration and Enhanced Multi-View Geometry for Reliable Mapping in Dynamic Environment

Islam,

Ibrahim,

Chin

et al. 2023

2023 7th International Conference on Imaging, Signal Processing and Communications (ICISPC)

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A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

Cited by 4 publications

References 32 publications

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes

Dynamic Object-Aware SLAM: YoloV8 Integration and Enhanced Multi-View Geometry for Reliable Mapping in Dynamic Environment

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